DE10362369B3 - Integrated model predictive control and optimization within a process control system - Google Patents

Abstract

A process control configuration system for use in fabricating or screening an integrated optimization and control block is provided that implements an optimization program and a multiple-input, multiple-output handler. The configuration system may allow a user to view or configure the optimizer or control program. A storage program can store data relating to multiple control and auxiliary variables and multiple manipulated variables to be used by the optimization program and/or the control program. A viewer may provide a user with a view of data related to the multiple control and auxiliary variables and the multiple manipulated variables.

Description

The present invention relates generally to process control systems, and more particularly to an optimized model prediction controller within a process control system.

Process control systems, such as distributed or scalable process control systems such as those used in chemical, petroleum or other processes, typically include one or more, communicating with each other, with at least one main or operator workstation and with one or more field devices via analog, digital or combined analog/digital buses coupled controller(s). The field devices, which can be, for example, valves, valve actuators, switches and transmitters (e.g. temperature, pressure and flow rate sensors) perform functions within the process such as opening and closing valves and measuring process parameters. The process controller receives signals representative of process measurements made by the field devices and/or other data pertaining to the field devices, uses that data to initiate a control program, and then generates control signals that are transmitted over the buses to the Field devices are sent to control the flow of the process. Data from the field devices and the controller is typically provided to one or more applications run by the operator workstation to allow an operator to perform any desired function related to the process, such as monitoring the current process state, modifying the process flow, etc.

Process controllers are typically programmed to perform various algorithms, sub-programs, or control loops (all of which are control routines) for each of a number of different loops dedicated to or included in a process, such as flow control loops, temperature control loops, pressure control loops, etc. Generally speaking, each such control loop comprises one or more input blocks such as an analog input (AI) function block, a single output control block such as a proportional, integral, derivative (PID) control block or a fuzzy control function block, and a single output block, such as an analog output (AO) function block. These control loops typically perform single input/single output control because the control block generates a single control output that is used to control a single process input such as valve position, etc. In some cases, however, the use of several independently operating single input/single output control circuits does not make sense because the process variables to be controlled are affected by more than one single process input and each process input can affect the status of many process outputs. An example of this might occur in a process where a tank is to be filled through two supply lines and emptied through a single outlet line, each line being controlled by a different valve, and where the temperature, pressure and flow rate of the tank are thus controlled that they are at or near set points. As indicated above, the control of the flow rate, temperature and pressure of the tank can be performed using a separate flow control loop, a separate temperature control loop and a separate pressure control loop. However, in this situation, the operation of the temperature control circuit when changing the setting of one of the supply valves to control the temperature in the tank can cause the pressure in the tank to rise, for example causing the pressure circuit to open the exhaust valve to regulate the pressure to lower. This action can then cause the flow control loop to close one of the intake valves, thereby affecting the temperature and causing the temperature control loop to take some further action. As is clear from this example, the single-input, single-output control loops cause the process outputs (in this case, flow rate, temperature, and pressure) to behave in an unacceptable manner, where the outputs fluctuate without ever reaching a steady state reach.

Model Predictive Control (MPC) or other types of advanced control have been employed to perform process control in situations where changes to a particular controlled process variable affect more than one process variable or process output. Since the late 1970's, many successful implementations of model predictive control have been reported and MPC has become the primary form of advanced multivariable control in the process industry. In addition, MPC control has been implemented in distributed control systems as distributed control system multi-layer software. the U.S. Patent Nos. 4,616,308 and 4,349,869 generally describe MPC controllers that may be used in a process control system.

Generally speaking, MPC is a multiple-input/multiple-output control strategy in which the effects of changing each of multiple process inputs are reflected in each of multiple process outputs measured, and these measured responses then used to generate a control matrix or model of the process. The process model or control matrix (which generally defines the steady state of the process) is mathematically inverted and then used in or as a multiple input/multiple output controller to control the process outputs based on changes made to the process inputs. In some cases, the process model is represented as a process output response curve (typically a step response curve) for each of the process inputs, and these curves may be generated based on a series of, for example, pseudo-random step changes provided to each of the process inputs. These response curves can be used to model the process in a known manner. Model predictive control is well known in the art and therefore its specifics will not be described here. However, MPC is generally described in Qin, S. Joe and Thomas A. Badgwell, "An Overview of Industrial Model Predictive Control Technology", AIChE Conference, 1996.

MPC turned out to be a very effective and useful control technique and was used in the context of process optimization. To optimize a process employing MPC, an optimizer minimizes or maximizes one or more process inputs determined by the MPC program in order to run the process at an optimal operating point. While this technique is computationally possible, in order to optimize the process from an economic standpoint, the process variables that have a significant impact on improving the economics of the process (e.g., process throughput or quality) must be selected. In order to run the process at an optimal operating point from a financial or economic point of view, many process variables, and not just a single process variable, must be controlled collectively.

Optimization using quadratic programming techniques or more familiar techniques such as floating point methods have been proposed as a solution to provide dynamic optimization with MPC. With these methods, an optimization solution is determined and the optimizer provides the controller with implementations in the controller outputs (i.e. the manipulated variables of the process), taking into account the process dynamics, existing constraints and optimization goals. However, this approach has a tremendous computational burden and is not feasible with the current level of technology.

In most cases when using MPC, the number of manipulated process variables available within the process (i.e. the control outputs of the MPC program) is greater than the number of controlled variables of the process (i.e. the number of process variables that are controlled need to be to be at a certain setpoint). As a result, there are usually several degrees of freedom available to handle optimization and constraints. Theoretically, in order to carry out such an optimization, values should be calculated that are expressed by process size, boundary conditions, limits and economic factors that determine an optimal operating point of the process. In many cases these process variables are constrained variables because they have limits relative to the physical properties of the process they affect within which they must be maintained. For example, a process variable that represents the tank fill level is limited to the maximum and minimum fill level that can be physically reached in the respective tank. An optimization function can compute costs and/or benefits associated with each of the constraint or auxiliary quantities to operate at a level where utility is maximized, cost is minimized, etc. The measurements of these auxiliary quantities can then be fed to the MPC Provided as inputs to the program and to be processed by the MPC program as control variables with a setpoint equal to the operating point for the auxiliary variable defined by the optimization program.

MPC provides the best performance, often required by the application only for quadratic control, where the number of control inputs to the process (i.e., the manipulated variables formed by the control program) equals the number of control variables to be controlled (i.e., the inputs to the controllers). In most cases, however, the number of mandatory auxiliary variables plus the number of process control variables is greater than the number of manipulated variables. Implementing MPC for such non-square configurations results in unacceptably poor performance.

It is believed that others have attempted to overcome this problem by selecting a set of control and forcing variables equal to the number of manipulated variables and generating the controller on-line or in-process to determine the next moves at the to determine variables. However, this technique is computationally expensive because it uses matrix inversion and cannot be used in some cases like MPC built as a function block in a process control device. Equally important, some combinations of inputs and outputs of the generated controller can result in an ill-conditioned controller, resulting in unacceptable operation. While the health of the controller can be checked and improved when the controller configuration is installed offline, this task is unduly burdensome for online operation and virtually impossible to accomplish at the controller level.

U.S. 2002 107 821 A1 relates to a system for conducting trade-off studies using multiple parameter selection. Candidates, each of which is assigned one or more criteria, are entered into the system via an input interface. In addition, test bases can be entered and linked to candidates and criteria to facilitate human understanding of test data and results. The criteria are weighted with pairwise comparisons to minimize errors introduced by test engineer judgment. Then the candidates are evaluated using pairwise comparison techniques, which are then subjected to sensitivity testing. Processed candidates and criteria are then made available to a system operator in operator selectable formats.

U.S. 5,930,762 A describes a computational method that manages risk in physical multi-parameter systems performing interconnected activities. At least one of these activities is risk-related in that it may have an out-of-bounds outcome level. The method specifies a course of action for the physical systems that makes it possible to prevent out-of-bounds result values for risk-related activities. The method assumes the existence of a multinational computational model that describes the physical systems and, according to a set of criteria, determines both possible and desirable levels of their activities.

U.S. 4,736,316 refers to a constraint-dependent control method and system for a large-scale refinery that allows on-line optimization of one or two plant inputs without the need for optimal setpoints by iterative methods generating a constraint-dependent control code that normalizes to the current time t is to quickly "drive" the optimized plant inputs to optimization without destabilizing the plant. The generation of such control code involves the evaluation of certain key components associated with elements of an optimization and stabilization control vector (OSC) from which the code is derived.

U.S. 5,612,866 A describes an asynchronous real-time controller that includes a start procedure that initializes parameters associated with a real-time system, an interrupt procedure that specifies an operation to be performed in response to an asynchronous interrupt signal from the real-time system, and a background procedure that continuously loops through a set of instructions and periodically generates a control action command derived from the interrupt procedure to be executed by the real-time system.

U.S. 6,049,738 A relates to a modeling support system for modeling a control model required for simulating a control object in which a plurality of data sets are stored in a first database for use in modeling a plurality of control models, divided by the data divider into a plurality of sub-data groups that each have the smallest statistical disparity and stored in a plurality of sub-databases each in the second database. The modeler constructs a control model using data in one of the plurality of sub-data sets, which is then evaluated by the model evaluator using data in another of the plurality of sub-data sets.

DE 100 48 360 A1 refers to a process control element adapted to be used as part of a process control routine implemented on a processor for controlling a process, the process control element including: a computer-readable medium; an advanced control function block stored on the computer-readable medium and adapted to be executed on the processor to implement multiple input/multiple output control of a process, the advanced control function block including: a first plurality of inputs, each input so is designed to receive a different one of a set of process parameters at a time; a second plurality of outputs, each output being adapted to be communicatively connected to a different process input for controlling the set of process parameters dung is brought; and control logic that generates a control signal at each of the second plurality of outputs in response to the first plurality of inputs.

DE 101 27 788 A1 discloses a process control system and a method for use in such, having multiple manipulated variables and many controlled and auxiliary variables that can be affected by changes in the manipulated variables, having a number of common features with the subject matter of the present application.

the U.S. 5,457,625 A discloses an optimization method performed in each cycle of operation of a process controller.

It is an object of the present invention to improve a process control system with an optimizer and a multiple input/multiple output controller such that a process can be brought to an optimal process operating point.

According to the present invention a process control configuration system according to claim 1, a method for using a process control system according to claim 18 and claim 22, a process control element according to claim 23 and a method for performing the control of a process according to claim 24 are provided.

In particular, a process control configuration system is provided for use in creating or viewing an integrated optimization and control block that runs an optimization program and a multiple input/multiple output handler. The configuration system may allow a user to view or configure the optimizer or scheduler. For example, a storage program can store data
relating to a plurality of controlled and auxiliary variables and a plurality of manipulated variables to be used by the optimizer and/or the control program, and a display program may present a user with a display relating to the data relating to the plurality of controlled and auxiliary variables and the plurality of manipulated variables.

In one embodiment, the memory program stores response data or response data for each of at least some of the control and auxiliary quantities. The response behavior data for a controlled or auxiliary variable may include data indicative of a respective response behavior of the controlled or auxiliary variable to respective manipulated variables. This response behavior can be step behavior, pulse behavior, increase behavior, etc., for example. The display program can display response data to the user. For example, the user can specify a manipulated variable and the viewer can display the response of one or more of the control and auxiliary variables to the specified manipulated variable.

In another aspect, a process control system for controlling a process includes a multiple input/multiple output controller and an optimizer. The multiple input/multiple output controller generates multiple control outputs during each cycle of operation of the process control system that are configured to control the process based on multiple measurement inputs from the process and based on a set of target values that the multiple input/multiple output controller provides during each cycle of operation of the process control system. The optimizer builds the set of target values for use by the multiple input/multiple output controller during each cycle of operation of the process control system. The optimizer attempts to minimize or maximize an objective function while maintaining a set of controlled variables within predetermined setpoint limits, a set of auxiliary variables within a set of predetermined auxiliary variable limits, and a set of manipulated variables within a set of predetermined manipulated variable limits. If the optimizer cannot determine a solution, the optimizer attempts to minimize or maximize the objective function while allowing at least one of the target limits to be violated.

In yet another aspect, a process control method for controlling a process having multiple manipulated variables and multiple control and auxiliary variables includes selecting a subset of control and auxiliary variables for use in performing the process control, at least one of the selected control and auxiliary variables being selected based thereon that it responds most strongly to one of the manipulated variables. A control matrix is generated using the selected control and auxiliary variables and the manipulated variables, and a controller matrix is generated from the control matrix. A Inputs to the controller include the selected control and auxiliary variables, and outputs from the controller include the manipulated variables. Optimization is performed by selecting a process operation point, the process operation point being determined by a set of target values for the selected control and auxiliary quantities to minimize or maximize an objective function. The controller is used to perform a multiple input/multiple output control process to form a set of manipulated variable values from the target values.

• 1 ist ein Blockschema eines Prozesssteuerungssystems einschließlich eines Steuermoduls mit einem fortgeschrittenen Controller-Funktionsblock, der einen Optimierer mit einem MPC-Controller beinhaltet;
• 2 ist ein Blockschema des fortgeschrittenen Controller-Funktionsblocks von 1 mit integriertem Optimierer und MPC-Controller;
• 3 ist ein Ablaufdiagramm, das eine Art und Weise darstellt, den integrierten Optimierer und den MPC-Controller-Funktionsblock von 2 festzulegen und zu installieren;
• 4 ist ein Ablaufdiagramm, das den Betrieb des integrierten Optimiereres und MPC-Controllers von 2 während eines Online-Prozessablaufs darstellt;
• 5 ist eine Bildschirmanzeige eines Konfigurationsprogramms, die einen fortgeschrittenen Steuer-/Regelblock in einem Steuermodul darstellt, das eine Prozesssteuerung durchführt;
• 6 ist eine Bildschirmanzeige einer Konfigurationsprogramms, die ein Dialogfeld darstellt, das die Eigenschaften des fortgeschrittenen Steuer-/Regelblocks von 5 anzeigt;
• 7 ist eine Bildschirmanzeige eines Konfigurationsprogramms, das eine Art und Weise darstellt, Eingaben in einen oder Ausgaben aus einem in der Anzeige von 5 gezeigten fortgeschrittenen Steuerfunktionsblock auszuwählen oder einzugeben;
• 8 ist eine Bildschirmanzeige, die von einem Konfigurationsprogramm bereitgestellt wird, das es einem Benutzer oder Bediener ermöglicht, eine aus einem Satz von Zielfunktionen zur Verwendung bei der Festlegung eines fortgeschrittenen Steuer-/Regelblocks auszuwählen;
• 9 ist eine Bildschirmanzeige eines Testbildschirms, der dazu verwendet werden kann, es einem Benutzer zu ermöglichen, ein Prozessmodell während des Festlegens eines fortgeschrittenen Steuer-/Regelblocks zu testen und zu erzeugen;
• 10 ist eine Bildschirmanzeige eines Konfigurationsprogramms, das mehrere Schrittantworten darstellt, die das Ansprechverhalten verschiedener Steuer- und Hilfsgrößen auf eine bestimmte Stellgröße anzeigen;
• 11 ist eine Bildschirmanzeige eines Konfigurationsprogramms, das eine Art und Weise des Auswählens der Steuer- oder Hilfsgrößen von 9 darstellt, die zuvor der Stellgröße zugeordnet wurden;
• 12 ist eine Bildschirmanzeige eines Konfigurationsprogramms, das mehrere Schrittantworten darstellt, die das Ansprechen derselben Steuer- oder Hilfsgröße auf andere der Stellgrößen anzeigt;
• 13 ist eine Bildschirmanzeige eines Konfigurationsprogramms, das eine andere Art und Weise darstellt, Steuer- oder Hilfsgrößen auszuwählen, die Stellgrößen zugeordnet werden sollen;
• 14 ist eine Bildschirmanzeige eines Konfigurationsprogramms, das eine andere Art und Weise darstellte, Steuer- oder Hilfsgrößen auszuwählen, die Stellgrößen zugeordnet werden sollen;
• 15 ist eine Bildschirmanzeige, die die Art und Weise darstellt, eine der Schrittantworten eines Modells zu kopieren, die zur Verwendung in einem anderen Modell kopiert werden soll;
• 16 ist eine Bildschirmanzeige, die die Art und Weise darstellt, eine Schrittantwortkurve zu sichten und zu ändern;
• 17 ist eine Bildschirmanzeige, die einen Dialogbildschirm darstellt, welcher dem Bediener während des Betriebs des fortgeschrittenen Steuer-/Regelblocks Daten zur Verfügung stellt; und
• 18 ist eine Bildschirmanzeige, die einen Diagnosebildschirm darstellt, der einem Benutzer oder Bediener zur Verfügung gestellt werden kann, um am fortgeschrittenen Steuer-/Regelblock eine Diagnose vorzunehmen.

Preferred embodiments are described below with reference to the drawings. Show it:

• 1 Figure 13 is a block diagram of a process control system including a control module with an advanced controller function block that includes an optimizer with an MPC controller;
• 2 is a block diagram of the advanced controller function block of 1 with integrated optimizer and MPC controller;
• 3 FIG. 12 is a flowchart showing one way to use the integrated optimizer and MPC controller function block of FIG 2 to define and install;
• 4 is a flow chart showing the operation of the integrated optimizer and MPC controller of 2 during an online process flow;
• 5 Figure 12 is a configuration program screen display showing an advanced control block in a control module that performs process control;
• 6 is a screen shot of a configuration program showing a dialog box showing the properties of the advanced control block of 5 indicates;
• 7 is a screen display of a configuration program that shows a way to get input into or output from a display of 5 select or enter advanced control function block shown;
• 8th is a screen display provided by a configuration program that allows a user or operator to select one of a set of target functions for use in specifying an advanced control block;
• 9 Figure 13 is a screen display of a test screen that can be used to allow a user to test and create a process model during the definition of an advanced control block;
• 10 Figure 13 is a screen display of a configuration program displaying a plurality of step responses indicative of the response of various control and auxiliary variables to a particular manipulated variable;
• 11 is a screen display of a configuration program showing a way of selecting the control or auxiliary variables of 9 represents that were previously associated with the manipulated variable;
• 12 Figure 13 is a screen display of a configuration program showing multiple step responses indicating the same control or auxiliary variable's response to other ones of the manipulated variables;
• 13 Figure 13 is a screen display of a configuration program showing another way of selecting control or auxiliary variables to be associated with manipulated variables;
• 14 Figure 13 is a screen display of a configuration program showing another way of selecting control or auxiliary variables to be associated with manipulated variables;
• 15 Figure 12 is a screen display showing the manner of copying one of a model's step responses to be copied for use in another model;
• 16 Figure 12 is a screen display showing the way to view and change a step response curve;
• 17 Figure 12 is a screen display showing a dialog screen that provides data to the operator during operation of the advanced control block; and
• 18 is a screen display that presents a diagnostics screen that can be made available to a user or operator to diagnose the advanced control block.

Now with reference to 1 a process control system 10, a process controller 11 communicatively connected to a data collection 12 and one or more host workstations or computers 13 (which may be any type of PC, workstation, etc.), each having a display screen 14. The controller 11 is connected via input/output (I/O) cards 26 and 28 to field devices 15-22. Data collection 12 may be any desired type of data collection unit with any desired type of memory and any desired or known software, hardware or firmware for storing data, and may be separate (as in 1 shown) or part of one of the workstations 13. The controller 11, which may be, for example, the DeltaV™ controller sold by Fisher-Rosemount Systems, Inc., is connected to the host computers 13 and the data collection 12 via an Ethernet connection or some other communications network 29, for example. Communications network 29 may be a local area network (LAN), wide area network (WAN), telecommunications network, etc., and may be implemented using wired or wireless technology. The controller 11 is communicative with the Field devices 15 - 22 connected.

Field devices 15-22 can be any type of device, such as sensors, valves, transmitters, actuators, etc., while I/O cards 26 and 28 can be any type of I/O device conforming to any desired communication or controller protocol. in the in 1 In the illustrated embodiment, field devices 15-18 are standard 4-20 ma devices that communicate with I/O card 26 over analog lines, while field devices 19-22 are intelligent devices, such as fieldbus field devices, that communicate over a digital bus communicate with the I/O card 28 using Fieldbus protocol communications. Of course, the field devices 15-22 could conform to any other standard or standards or protocols, including those that may be developed in the future.

The controller 11, which may be one of many distributed controllers within the plant 10, has within it at least one processor, implements or oversees one or more process control programs, which may include control circuitry stored therein or otherwise associated therewith. The controller 11 also communicates with the devices 15-22, the main computers 13 and the data collection 12 to control a process in any desired manner. It should be noted that any control programs or elements described herein may have portions thereof that are implemented or executed by other controllers or devices, if desired. Likewise, the control programs or elements described herein to be implemented in process control system 10 may take any form, including software, firmware, hardware, etc. For purposes of this discussion, a process control element may be any part or portion of a process control system, including, for example, a program, block, or module stored on any computer-readable medium. Control programs, which can be modules or any part of a control process, such as a subprogram, parts of a subprogram (like command lines), etc., can be implemented in any desired software format, such as that using ladder logic, sequence function curves, function block diagrams, object-oriented programming, or uses any other software programming language or any other software design paradigm. Likewise, the control programs may be hard-coded into, for example, one or more EPROM/s, EEPROM/s, Application Specific Integrated Circuits (ASICs), or any other hardware or firmware element. Furthermore, the control programs can be defined using any design tools including graphical design tools or any other type of software/hardware/firmware programming or design tools. In this way, the controller 11 can be configured to implement a control strategy or program in any desired manner.

In one embodiment, the controller 11 implements a control strategy that employs what are commonly referred to as functional blocks, each functional block being a part or object of a higher-level control program and together with other functional blocks (via communications called shortcuts or "links") operates to implement process control loops within the process control system 10 . The function blocks typically perform an input function, such as that associated with a transmitter, sensor, or other measurement of process parameters, a control function, such as that associated with a control program that performs PID, fuzzy control, etc., or a Output function showing the operation any device, such as a valve, to perform any physical function within the process control system 10. Of course, there are hybrid and other types of functional blocks. Function blocks can be stored and run by the controller 11, which is typically the case when these function blocks are used for or in connection with standard 4 - 20 ma devices and some types of intelligent field devices such as HART devices, or can be stored in and implemented by the field devices themselves, which may be the case with Fieldbus devices. While the description of the control system is described here using a function block control strategy that employs an object-oriented programming paradigm, the control strategy or control circuits or modules could also be implemented or designed using other programming conventions, such as LAD logic, sequence function curves, etc., or using any other programming language or any other programming paradigm.

As indicated by the extended block 30 of 1 1, controller 11 may include multiple single loop control programs, shown as programs 32 and 34, and may implement one or more advanced control loops, shown as control loop 36. Each such circuit is typically referred to as a control module. The single loop control programs 32 and 34 are shown as performing single loop control using a single input/single output fuzzy control block and a single input/single output PID control block, respectively, connected to appropriate analog input (AI) and analog input (AI) function blocks Output (AO) which may be associated with process control devices such as valves, measurement devices such as temperature and pressure transmitters, or any other device within the process control system 10. The advanced control loop 36 is illustrated as including an advanced control block 38 having inputs communicatively connected to various AI function blocks and outputs communicatively connected to various AO function blocks, although the inputs and outputs of the advanced control /rule blocks 38 may also be communicatively connected to any other desired functional blocks or control elements to receive other types of inputs and provide other types of control outputs. As will be further described, advanced control block 38 may be a control block that includes a model predictive handler with an optimizer to perform optimized control of the process or a portion of the process. While advanced control block 38 is described herein as including a model predictive (MPC) control block, advanced control block 38 could be any other multiple-input, multiple-output control program or procedure such as a program for neural network modeling or control, a multivariable control program in fuzzy program logic, etc. It will be clear that the in 1 The functional blocks illustrated, comprising the advanced control block 38, may be executed by the controller 11, or alternatively may be located in and executed by any other processing device, such as one of the workstations 13 or even one of the field devices 19-22.

As in 1 As illustrated, one of the workstations 13 includes an advanced control block generator program 40 which is used to define, download and implement the advanced control block 38 . While advanced control block generator program 40 may be stored in memory within workstation 13 and executed therein by a processor, this program (or any portion thereof) may additionally or alternatively reside in any other device within process control system 10, if that is so desired can be stored and executed by it. In general terms, the advanced control block generator program 40 includes a control block specification program 42 that specifies an advanced control block, as described later herein, and that incorporates this advanced control block into the process control system process modeling program 44 that creates a process model for the process or part thereof based on data collected by the advanced control block, a control logic parameter setting program 46 that creates control logic parameters for the advanced control block from the process model, and the stores and downloads these control logic parameters in the advanced control block for use in process control, and an optimizer 48 which defines an optimization program for use with the advanced control block. Of course, the programs 42, 44, 46 and 48 can consist of a number of different programs, such as a first program that defines an advanced control element whose control inputs are adapted to receive process outputs and whose control outputs are adapted to send control signals to the process inputs to send, a second program that enables a user to download and communicatively include the advanced control within the process control program (which may be any desired configuration program), a third program that uses the advanced control to impart excitation waveforms to each of the process inputs, a fourth program that uses the advanced control to collect data reflecting the response of each of the process outputs to the excitation waveforms, a fifth program that itself selects or allows a user to select a set of inputs for the advanced control block, a sixth program that specifies a process model, a seventh program that forms advanced control logic parameters from the process model, an eighth program that defines the advanced control logic and , if necessary, injects the process model into the advanced control to allow the advanced control to control the process, and a ninth program that selects itself or allows a user to use an optimizer in the advanced Select control / regulation block 38.

2 Figure 12 illustrates a more detailed block diagram of one embodiment of the advanced control block 38 communicatively coupled to a process 50, it being understood that the control block 38 generates a set of manipulated variables MVs that are made available to other functional blocks, which in turn are connected to control inputs of the process 50. As in 2 As shown, the advanced control block 38 includes an MPC controller block 52, an optimizer 54, a goal translation block 55, a step response model or control matrix 56, and an input manipulation/filter block 58. The MPC controller 52 can be any Act standard, an M-by-M MPC program or procedure (where M can be any number greater than one) with the same number of inputs and outputs. The MPC controller 52 receives as inputs a set of N control and auxiliary variables CV and AV (which are vectors of values) as measured in the process 50, a set of disturbance variables DV which are known or expected changes or disturbances, applied to the process 50 at some time in the future, and a set of steady state target and auxiliary quantities CVT and AV _T provided by the target translation block 55 . The MPC controller 52 uses these inputs to determine the set of M manipulated variables MV (in the form of control signals) and provides the manipulated variable MV signals to control the process 50 .

In addition, the MPC controller 52 calculates a set of predicted steady-state control variables CV _SS and predicted steady-state auxiliary variables AV _SS and provides them, together with a set of predicted steady-state manipulated variables MV _SS , which represent the predicted values of the controlled variables CV, the auxiliary variables AV and the manipulated variables MV available to the input processing/filtering block 58 in the prediction horizon. The input processing/filtering block 58 processes the determined prediction steady-state values of the control, auxiliary, and manipulated variables CV _SS , AV _SS , and MV _SS to reduce the effects of noise and unpredicted disturbances on these variables. Of course, the input processing/filtering block 58 may include a low-pass filter or any other input processing that reduces the effects of noise, modeling errors, and interference on these values, and provides the filtered control, auxiliary, and manipulated variables CV _SSfil , AV _SSfil , and MV _SSfil the optimizer 54.

The optimizer 54 in this example is a linear programming (LP) optimizer that uses an objective function (OF), which may be provided by a selection block 62, to perform process optimization. Alternatively, the optimizer 54 could be a quadratic programming optimizer, which is an optimizer with a linear model and a quadratic objective function. In general terms, the objective function OF specifies costs and benefits associated with each of a number of control, auxiliary, and manipulated variables, and the optimizer 54 sets target values for these variables by maximizing or minimizing the objective function. The selection block 62 may select the objective function OF, which is provided to the optimizer 54 as one of a set of pre-stored objective functions 64 that mathematically represent various ways of defining the optimal flow of the process 50 . For example, one of the pre-stored objective functions 64 may be configured to maximize plant profit, another of the objective functions 64 may be configured to minimize the use of a particular raw material that is in shortage of supply, while still another of the objective functions 64 can be configured to maximize the quality of the product to be manufactured as part of the process 50. In general terms, the objective function uses costs or benefits associated with each movement of a control, auxiliary, and manipulated variable to determine the most optimal process operating point within a set of acceptable points as defined by the CV setpoints or ranges and the limits of the Auxiliary and manipulated variables AV and MV are defined. Of course, any desired objective function may be used in place of or in addition to those described herein, including objective functions that each optimize several issues such as raw material usage, profitability, etc. in some way.

In order to select one of the objective functions 64, a user or operator can provide an indication of the objective function 64 to be used by entering that objective function on an operator or user terminal (such as one of the workstations 13 of 1 ) selects which selection is made available to the selection block 62 via an input 66 . In response to the input 66, the selection block 62 provides the selected objective function OF to the optimizer 54. Of course, the user or operator can change the objective function used during the process flow. If desired, a default objective function can be used in cases where the user does not provide or select an objective function. A possible default objective function is explained in more detail below. Although shown as part of the advanced control block 38, the various target functions can also be implemented in the operator terminal 13 of FIG 1 may be stored, and one of these objective functions may be made available to the advanced control block 38 during the definition or generation of that block.

In addition to the objective function OF, the optimizer 54 receives as inputs a set of control variable setpoints (which are typically operator specified setpoints for the control variables CV of the process 50 and can be changed by the operator or another user) and a range and weight or priority that/ which is assigned to the control variables CV. The optimizer 54 also receives a set of range or constraint limits and a set of weights or priorities for the auxiliary variables AV and a limit set for the manipulated variables MV used to control the process 50 . In general terms, the auxiliary and manipulated variable ranges define the limits (typically based on the physical properties of the plant) for the auxiliary and manipulated variable ranges, while the controlled variable ranges provide a range within which the controlled variables can be used for satisfactory control of the process can work. The weights for the control and auxiliary variables determine the relative importance of the control and auxiliary variables in relation to each other in the optimization process and can be used under certain circumstances to allow the optimizer 54 to generate a control target solution if some of the constraint limits are violated will.

During operation, the optimizer 54 may use a linear programming (LP) programming technique to perform the optimization. As is well known, linear programming is a mathematical technique for solving a set of linear equations or inequalities that maximizes or minimizes some additional function, called the objective function. As discussed previously, the objective function can express an economic value such as cost or benefit, but can express other objectives instead. Furthermore, as is assumed, the steady state gain matrix defines the steady state gain for each possible pair of the manipulated and control and auxiliary variables. In other words, the steady-state gain matrix defines the steady-state gain in each control and auxiliary variable for a unit change in each of the manipulated and disturbance variables. This steady state gain matrix is generally an N by M matrix, where N is the number of control and auxiliary variables and M is the number of manipulated variables used in the optimization routine. In general, N can be greater than, equal to, or less than M, with the most general case being that N is greater than M.

Using any known or standard LP algorithm or method, the optimizer 54 iterates the determination of the set of target manipulated variables MVT (as determined by the steady-state gain matrix) that maximize or minimize the selected objective function OF while resulting in a process flow that Fulfills the setpoint range limits of the control variable CV, the forced limits of the auxiliary variable AV and the limits of the manipulated variable MV or is within them. In one embodiment, the optimizer 54 even determines the change in manipulated variables and uses the indication of the prediction steady state controls, auxiliary and manipulated variables CV _SSfil , AV _SSfil and MV _ssfil to determine the changes in process flow from its current flow, ie, the dynamic flow of the MPC scheduler during the process of reaching the target or optimal process operation point. This dynamic flow is important because it must be ensured that none of the constraint limits are violated during the movement from the current operation point to the target operation point.

worin :

• Q = Gesamtkosten/-nutzen
• P = Nutzenvektor im Zusammenhang mit den AVs und CVs
• C = Kostenvektor im Zusammenhang mit MVs
• A = Verstärkungsmatrix
• ΔMV = Vektor für die berechnete Veränderung bei den MVs

In one embodiment, the LP optimizer 54 may be designed to minimize an objective function of the form: $$\text{Q}={\text{p}}^{\text{t}}\ast \text{A}\ast \Delta \text{MV}+{\text{C}}^{\text{t}}\Delta \text{MV}$$

in which :

• Q = total cost/benefit
• P = utility vector associated with the AVs and CVs
• C = cost vector associated with MVs
• A = gain matrix
• ΔMV = vector for the calculated change in MVs

The utility values are positive numbers and the cost values are negative numbers to indicate their impact on the goal. Using this objective function, the LP optimizer 54 calculates the changes in the manipulated variables MV that minimize the objective function while ensuring that the controlled variables CV remain within a range of their target setpoint, that the auxiliary variables AV stay within their lower and upper constraint limits are located, and that the manipulated variables MV lie within their upper and lower limits.

worin: $$\Delta CV\left(t+p\right)=\left[\begin{array}{c}\Delta C{V}_i\\ \dots \\ \Delta C{V}_n\end{array}\right]$$

vorhergesagte Änderungen in Ausgaben am Ende des Vorhersagehorizonts (t + p) bezeichnet, $$A=\left[\begin{array}{c}{a}_{ll}\dots a_{lm}\\ \dots \dots \dots \dots \\ a_{nl}\dots a_{nm}\end{array}\right]$$

der Prozessbeharrungszustand m mal n Verstärkungsmatrix ist, $$\Delta MV\left(t+c\right)=\left[\begin{array}{c}\Delta m{v}_l\\ \dots \\ \Delta m{v}_n\end{array}\right]$$

Veränderungen bei den Stellgrößen am Ende des Steuerhorizonts (t+c) bezeichnet.One method of optimization that can be used uses incremental values of manipulated variables at the current time (t) and a sum of manipulated variables over the control horizon, with the incremental values of the control and auxiliary variables at the end of the forecast horizon and not instantaneous Place values are determined, as is typical in LP applications. Of course, the LP algorithm can be modified appropriately for this variant. In any event, the LP optimizer 54 may use a steady state model, and as a result a steady state condition is required for this application. With a prediction horizon, such as is typically used in MPC development, future steady state is guaranteed for a self-regulatory process. A possible process prediction steady state equation for an m by n input-output process with a prediction horizon p, a control horizon c, expressed in incremental form, is: $$\Delta CV\left(t+p\right)=A\ast \Delta MV\left(t+c\right)$$

wherein: $$\Delta CV\left(t+p\right)=\left[\begin{array}{c}\Delta C{V}_i\\ ...\\ \Delta C{V}_n\end{array}\right]$$

denotes predicted changes in outputs at the end of the forecast horizon (t + p), $$A=\left[\begin{array}{c}{a}_{ll}...a_{lm}\\ ............\\ a_{nl}...a_{nm}\end{array}\right]$$

the process steady state is m by n gain matrix, $$\Delta MV\left(t+c\right)=\left[\begin{array}{c}\Delta m{v}_l\\ ...\\ \Delta m{v}_n\end{array}\right]$$

changes in the manipulated variables at the end of the control horizon (t+c).

The vector ΔMV(t+c) represents the sum of the changes over the control horizon made by each controller output mv _i such that $$\begin{array}{l}\Delta m{v}_i={\displaystyle \sum _{j=l}^{c}m{v}_i\left(t+j\right)}\\ \text{i}=\mathrm{1.2,}...,\text{m}\end{array}$$

The changes should correspond to limits for both the manipulated variables MV and the control variables CV (here the auxiliary variables are treated as control variables): $$\begin{array}{l}M{V}_{min}\le M{V}_{mOmen\text{tan}}+\Delta MV\left(t+c\right)\le M{V}_{\text{Max}}\\ C{V}_{min}\le C{V}_{vO\mathrm{right}He\mathrm{right}GesaGt}+\Delta CV\left(t+p\right)\le C{V}_{\text{Max}}\end{array}$$

worin:

• UCV der Kostenvektor für eine Änderungseinheit beim Prozesswert der Steuergröße CV ist; und
• UMV der Kostenvektor für eine Änderungseinheit beim Prozesswert der Stellgrößen MV ist.

In this case, the objective function for maximizing product value and minimizing raw material cost can be defined together as: $$\underset{\text{at least}}{Q}=-uC{V}^T\ast \Delta CV\left(t+p\right)+uM{V}^T\ast \Delta MV\left(t+c\right)$$

wherein:

• UCV is the cost vector for a unit change in the process value of the control variable CV; and
• UMV is the cost vector for a unit change in the process value of the manipulated variable MV.

Using the first equation above, the objective function can be expressed in terms of manipulated variables MV as: $$\underset{\text{at least}}{Q}=-uC{V}^T\ast A\ast \Delta MV\left(t+c\right)+uM{V}^T\ast \Delta MV\left(t+c\right).$$

To find an optimal solution, the LP algorithm computes the objective function for an initial vertex in the range defined by this equation, and improves the solution at each next step until the algorithm finds the vertex with the highest (or lowest) value of the objective function as optimal solution determined. The determined optimal manipulated variable values are applied as the target manipulated variables MV _T to be achieved within the control horizon.

In general terms, running the LP algorithm on the prepared matrix yields three possible outcomes. First, there is a single solution for the target manipulated variable MV _T . Second, the solution is unbounded, which should not be the case if the control or auxiliary has a high and a low bound. Third, there is no solution, which means that the bounds or boundary conditions imposed on the auxiliary quantities are too tight. To handle the third case, the boundary conditions can be relaxed to get a solution. The basic assumption is that the limits set for the manipulated variables (high/low limits) cannot be changed by the optimizer. The same applies to the boundary conditions or limits of the auxiliary quantities (high/low limits). However, the optimizer may refrain from driving the control variable CV to the specified set points (CV set point control) and the control variables to any other values within a range of or around the set point (CV range control). In this case, the values of the control variables can be settled in a range rather than at a specific target value. If there are multiple ancillaries AV violating their constraints and skipping from CV setpoint control to CV range control, it is also possible to relax or ignore the constraints of the ancillaries based on the intended weight or priority assignments. In one embodiment, a solution could be determined by minimizing the squared error of the auxiliaries, thereby allowing each of them to violate their respective constraint, or by sequentially relinquishing the constraints of the lowest priority auxiliaries.

As stated above, the objective function OF can be selected by the control block generator program 40 or can be set by default. A procedure for setting such a default setting is given below. In particular, while it is desirable to provide the optimization capability, many situations may require that only setpoint values for the controlled variables be maintained in a manner that still accounts for the operational and manipulated variable processing constraints. For these applications, block 38 can be configured to act solely as an MPC function block. To provide for this ease of use, a standard objective function 'Operate' can be generated automatically, with standard costs associated with the various quantities therein along with standard weights for the auxiliary quantities AV. These standards can set all costs for the auxiliary variables AV and the manipulated variables MV the same or can provide any other cost allocation to the auxiliary and manipulated variables AV and MV. If an expert option is chosen, the user can specify additional optimization selections and define their associated costs for different objective functions 64. The expert user then also has the option of changing the weightings of the standard auxiliary and control variables AV and CV of the standard target function.

In one embodiment, if, for example, no economic specifications are defined for the process configuration, the objective function can be built automatically from the MPC configuration. In general, the objective function can be constructed using the following formula: $$C{D}^T=C^T+P^T\ast A=\left[{\mathrm{<figure-callout\; id="1"\; label="C"\; filenames="DE000010362369B3\_0017.png,DE000010362369B3\_0020.png"\; state="\{\{state\}\}">C</figure-callout>}}_1,...C_j,...C_m\right]+\left[{\displaystyle \sum _{i=1}^n{p}_i{a}_{\mathrm{<figure-callout\; id="1"\; label="i"\; filenames="DE000010362369B3\_0017.png,DE000010362369B3\_0020.png"\; state="\{\{state\}\}">i</figure-callout>}1},}{\displaystyle \sum _{i=1}^n{p}_i{a}_{\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE000010362369B3\_0020.png,DE000010362369B3\_0021.png"\; state="\{\{state\}\}">i</figure-callout>}2},...{\displaystyle \sum _{i=1}^n{p}_i{a}_{im},}}\right]=\left[C{D}_l,...C{D}_j,...C{D}_m\right]$$

• p_j = -1, wenn der Sollwert auf niedrige Grenze festgelegt oder Minimieren ausgewählt wurde; und
• p_j = 1, wenn der Sollwert auf hohe Grenze festgelegt oder Maximieren gewählt wurde.

The quantities C _j and p _j can be defined from the configuration settings. In particular, assuming that the setpoint of the control variable CV can only be set to LL (Low Limit) or HL (High Limit), the value p _j is defined as follows:

• p _j = -1 if setpoint is set to low limit or minimize is selected; and
• p _j = 1 if setpoint is set to high limit or maximize is selected.

• C_j = 1, wenn MVT auf die hohe Grenze festgelegt oder Maximieren gewählt wurde;
• C_j = -1, wenn MVT auf die niedrige Grenze festgelegt oder Minimieren gewählt wurde, und
• C_j = 0, wenn MVT nicht definiert ist.

Assuming no configuration data is entered for the auxiliary variables AV, then p _j =0 for all auxiliary variables. Similarly for the manipulated variables MV, the value C _j depends on whether the preferred target manipulated variable MVT is defined or not. If the preferred target manipulated variable MVT is fixed, the following applies:

• C _j = 1 if MVT is set to the high limit or maximize is selected;
• C _j = -1 when MVT is set to the low limit or minimize is selected, and
• C _j = 0 if MVT is undefined.

worin S willkürlich oder heuristisch ausgewählt ist. Typischerweise ist S größer als Eins und kann sich im Bereich von Zehn befinden.If desired, the selection of the use of the optimizer 54 may be adjustable in conjunction with the MPC controller 52 to thereby provide a degree of optimization. In order to accomplish this function, the change in the manipulated variables MV used by the controller 52 can be changed by placing different weights on the change in the manipulated variables MV determined by the MPC controller 52 and the optimizer 54 . Such a weighted combination of the manipulated variables MV is referred to here as the effective MV (MV _eff ). The effective MV _rms can be determined as: $$\Delta M{V}_{eƒƒ}=M{V}_{mpc}\left(a/S\right)+\Delta M{V}_{Opt}\left(1-a\right){}_{0\le a\le \mathrm{1,}}$$

where S is chosen arbitrarily or heuristically. Typically, S is greater than one and can be in the range of ten.

Here the optimizer with α = 0 contributes to the effective output as set at the time of definition. With α = 1, the controller contributes only dynamic MPC control. Of course, the range between 0 and 1 provides different optimizer and MPC control contributions.

The default objective function described above can be used to run the optimizer in various possible modes of operation thereof. In particular, if the number of control variables CV matches the number of manipulated variables MV, the expected behavior with the standard setting is that the setpoints of the controlled variable CV are retained as long as the auxiliary variables AV and manipulated variables MV are expected to be within their limits. If an auxiliary or manipulated variable is predicted to violate its limit, the control variable work setpoints are varied within their range to prevent these limits from being violated. In this case, if the optimizer 54 cannot find a solution that keeps the auxiliary and manipulated variable limits while keeping the controlled variables within their range, the controlled variables are kept in their range while the auxiliary variables are allowed to deviate from their constraint limits. When finding the best solution, those auxiliary variables AV that are likely to violate a limit are then treated in the same way and their average limit deviation is minimized.

To achieve this behavior, the default cost/benefits used by the objective function are automatically adjusted such that the control variables CV are assigned a utility of 1 when the range is defined to allow deviation below target and a utility from -1 if the range is defined to allow deviation above the target value. The auxiliary variables AV located within the limits are assigned a benefit of 0 and the manipulated variables are assigned costs of 0.

If the number of control variables CV is less than the number of manipulated variables MV, then the additional degrees of freedom can be used to address the requirements associated with the final rest position of the configured manipulated variable MV. Here, the control variable setpoints (if CV control variables are defined) are retained as long as the auxiliary and manipulated values are expected to remain within their limits. The average deviation of the manipulated variables from the configured final idle position is minimized. If one or more of the auxiliary and manipulated variables is predicted to violate its limit, then the control variable work setpoints are changed within their ranges to prevent violating those limits. If there are multiple solutions to this condition, then the one used for control will minimize the mean deviation of the manipulated variables from the configured final rest position.

If the optimizer 54 cannot find a solution (i.e., there is no solution) that satisfies the auxiliary and manipulated variable limits while keeping the control limits within their range, then the controlled variables are kept within the range while the auxiliary variables are allowed to deviate from their constraint limits. When finding the best solution, those auxiliary quantities that are likely to violate a limit are then treated equally and their average limit deviation is minimized. To achieve this behavior, the default cost/benefits used by the objective function are automatically adjusted such that the controls are assigned a utility of 1 when the range is defined to allow deviation below target and a utility of -1 if the range is defined to allow deviation above the target value. The auxiliary variables are assigned a benefit of 1 or -1 and the manipulated variables are assigned costs of 0.1.

In any event, after execution, the optimizer 54 provides a set of optimal or target manipulated variables MVT to the goal translation block 55, which uses the steady state gain matrix to determine the target steady state controls and auxiliary variables resulting from the target manipulated variables MVT. This implementation is computationally problem-free, since the steady-state gain matrix defines the interactions between the manipulated variables and the control and auxiliary variables and can therefore be used to determine only the target control variables and auxiliary variables CVT and AV _T from the defined target (steady-state) manipulated variables MVT.

Once determined, at least a subset N of the target control and auxiliary variables CVT and AV _T are provided as inputs to the MPC controller 52 which, as previously noted, uses these target values CVT and AV _T to generate a new set of steady-state manipulated variables MV _SS (over the control horizon), which drives the current control and auxiliary variables CV and AV to the target values CVT and AV _T at the end of the forecast horizon. Of course, as is well known, the MPC controller varies the manipulated variables in steps in an attempt to reach the steady-state values for these quantities MV _SS , which in theory will be the target manipulated variables MVT determined by the optimizer 54 . Since the optimizer 54 and MPC controller 52 operate during each process scan as previously described, the target values of the manipulated variables MVT can change from sample to sample, and as a result the MPC controller can never actually achieve any particular one of these sets of target manipulated variables MVT , especially not when there is noise, unexpected disturbances, changes in the process 50, etc. Nevertheless, the optimizer 54 always drives the controller 52 to move the manipulated variables MV towards an optimal solution.

As is known, the MPC controller 52 includes a control process prediction model 70, which may be an N by M + D step response matrix (where N is the number of control variables CV plus the number of auxiliary variables AV, M is the number of manipulated variables MV, and D is the number of Disturbances DV is). The control process prediction model 70 produces in an output 72 a previously calculated prediction for each of the control and auxiliary variables CV and AV, and a vector summer 74 subtracts these prediction values for the current time from the actually measured values of the control and auxiliary variables to in the input 76 generate an error or correction vector.

The control process prediction model 70 then uses the N by M + D step response matrix to predict, for each of the control and auxiliary variables CV and AV, a future control parameter over the prediction horizon based on the disturbance and manipulated variables provided to other inputs of the control process prediction model 70. The control process prediction model 70 also provides the prediction steady-state values of the control and auxiliary variables CV _SS and AV _SS to the input processing/filtering block 58.

A target control block 80 determines a target control vector for each of the N target control variables and auxiliary variables CVT and AV _T provided to it by the target translation block 55 using a trajectory filter 82 previously created for the block 38 . In particular, the trajectory filter provides a unit vector that defines the manner in which the control and auxiliary variables are to be driven to their target values over time. The target control block 80 uses this unit vector and the target variables CVT and _{AVT to generate a dynamic target control vector for each of the control and auxiliary variables that defines the changes in the target variables CVT and AV T over} the period defined by time of the forecast horizon is set. A vector summer 84 then subtracts the future control parameter vector for each of the controls and assists CV and AV from the dynamic control vectors to define an error vector for each of the controls and assists CV and AV. The future error vector for each of the control and auxiliary variables CV and AV is then provided to the MPC algorithm, which operates to select the steps of the manipulated variable MV that, for example, have the smallest squared error over the control horizon for the manipulated variables MV and the forecast horizon for the control and auxiliary variables CV and AV are minimized. Of course, the MPC algorithm or controller uses an M by M process model or control matrix that is derived from the ratios between the N control and auxiliary variables input to the MPC controller 52 and the M is formed from the manipulated variables output by the MPC controller 52 .

More precisely, the MPC algorithm working with the optimizer has two main goals. First, the MPC algorithm tries to minimize the control variable error with minimal manipulated variable movements within operation constraints, and second, it tries to achieve optimal steady state manipulated variable values set by the optimizer and the target controlled variable values calculated directly from the optimal steady state manipulated variable values.

worin:

• CV(k) der gesteuerte, ausgegebene Vorhersagevektor ist, der um einen p-Schritt voraus ist;
• R(k) der Referenztrajektorien(sollwert)vektor ist, der um einen p-Schritt voraus ist;
• ΔMV(k) der inkrementale Steuerbewegungsvektor ist, der um einen c-Schritt voraus ist;
• Γ^y = diag{Γ^y ₁, ..., Γ^y _p} eine Penalty-Matrix für den gesteuerten Ausgabefehler ist,
• Γ^u = diag{Γ^u ₁, ..., Γ^u _c} eine Penalty-Matrix für die Steuerbewegungen ist,
• p der Vorhersagehorizont (Schrittanzahl) ist;
• c der Steuerhorizont (Schrittanzahl) ist; und

To satisfy these goals, the original unconstrained MPC algorithm can be extended to include the MV goals in the least squares solution. The objective function for this MPC controller is: $$fOR\mathrm{<figure-callout\; id="1"\; label="M\; E\; L"\; filenames="DE000010362369B3\_0017.png,DE000010362369B3\_0020.png"\; state="\{\{state\}\}">M</figure-callout>}EL1$$

wherein:

• CV(k) is the gated output prediction vector that is p-step ahead;
• R(k) is the reference trajectory (setpoint) vector ahead by one p-step;
• ΔMV(k) is the incremental control motion vector one c-step ahead;
• Γ ^y = diag{Γ ^y ₁ , ..., Γ ^y _p } is a penalty matrix for the controlled output error,
• Γ ^u = diag{Γ ^u ₁ , ..., Γ ^u _c } is a penalty matrix for the control movements,
• p is the forecast horizon (step number);
• c is the control horizon (step number); and

Γ ⁰ a penalty for the error of the controller output moves across the control horizon relative to the optimal target change of MV defined by the optimizer. For simplicity of illustration, the objective function for a single input, single output (SISO) control is shown.

As can be seen, the first two terms are the objective function for the unconstrained MPC controller, while the third term imposes an additional constraint that equates the sum of the controller output moves to the optimal objectives. In other words, the first two terms set goals for dynamic controller operation, while the third term set steady-state optimization goals.

worin:

• ΔMV(k) die Veränderung bei der MPC-Controllerausgabe zu einem Zeitpunkt k ist;
• K_ompc die optimierte MPC-Controllerverstärkung ist;
• S^u ist die Prozessdynamikmatrix, die aus den Schrittantworten der Dimension p × c für ein SISO-Modell und p*n × c*m für ein Mehrfacheingabe-/Mehrfachausgabe-MIMO-Modell mit m gestellten Eingaben und n gesteuerten Ausgaben aufgebaut ist.

Note that the general solution for this controller, similar to that for the unconstrained MPC controller, can be expressed as: $$fO\mathrm{right}mel2$$

wherein:

• ΔMV(k) is the change in MPC controller output at time k;
• K _ompc is the optimized MPC controller gain;
• S ^u is the process dynamics matrix constructed from the step responses of dimension p×c for a SISO model and p*n×c*m for a multi-input/multiple-output MIMO model with m inputs and n controlled outputs.

For an optimized MPC, the dynamics matrix is expanded to size (p+1)×m for the SISO model and (p+m)*n×c* for the MIMO model to accommodate the MV error. E _p+1 (k) is the CV error vector over the prediction horizon and the error of the sum of the controller output movements over the control horizon relative to the optimal target change of MV. The matrix Γ summarizes the matrix Γ ^y and Γ ⁰ and is a square matrix of dimension (p+1) for a SISO controller and [n(p+m)] for the multivariable controller. The superscript T denotes a transposed matrix.

It has been determined that since the optimizer 54 optimizes CV and AV based on all control and auxiliary variables to determine a target set of manipulated variables MVT that define a single optimum operating point, it does not matter that the MPC controller 52 only under Using a subset of the control and auxiliary variables CV and AV in its control matrix works to actually produce the manipulated variables output therefrom because when the controller 52 drives the selected subset of the control and auxiliary variables CV and AV to their assigned destinations, the other des complete set of control and auxiliary variables will also be at their target values. As a result, it was found that a square (M by M) MPC controller with an M by M control matrix can be used with an optimizer that uses a rectangular (N by M) process model to perform process optimization. This allows standard MPC control methods to be used with standard optimization methods without having to invert a non-square matrix with the accompanying approximations and the dangers associated with such mapping methods in a controller.

• ΔMVT - optimale Zielveränderung von MV
• ΔCV - CV-Veränderung zur Erlangung einer optimalen MV. CV-Veränderung wird implementiert, indem CV-Sollwerte verwendet werden.

In one embodiment, when the MPC controller is squared, that is, the number of manipulated variables MV equals the number of controlled variables CV, the target manipulated variable MV can be effectively achieved by changes in the CV values as follows: $$\Delta CV=A\ast \Delta MVT$$

• ΔMVT - optimal target change of MV
• ΔCV - CV change to obtain optimal MV. CV manipulation is implemented using CV setpoints.

In operation, the optimizer 54 establishes and updates steady-state targets for the unconstrained MPC controller on each sample. Thus, the MPC controller 52 performs the unconstrained algorithm. Since the targets CVT and _AVT are set in a way that takes into account constraints as long as a feasible solution exists, the controller operates within constraint limits. Therefore, optimization is an integral part of the MPC controller.

the 3 and 4 Figure 12 depicts a flowchart 90 showing the steps involved in performing integrated model predictive control and optimization. The flowchart 90 is generally divided into two sections 90a ( 3 ) and 90b ( 4 ) showing functions that appear before the process flow (90a) and functions that appear during the process flow (90b), eg during each scan of the process flow. Prior to process flow, an operator or technician takes a number of steps to fabricate the advanced control block 38 including an integrated MPC controller and optimizer. In particular, at block 92 an advanced control template may be selected for use as advanced control block 38 . The template can be stored in or copied from a library in a configuration application on the user interface 13, and can contain the general mathematical and logical functions of the MPC controller program 52 and the optimizer 54, without the specific MPC, process models and steady state gain or control matrices and the special objective function. This advanced control template can be placed in a module with other blocks, such as input and output blocks configured to interface with devices within the process 50, as well as other types of functional blocks such as control blocks, including PID, neural network - and fuzzy control blocks communicate. It will be appreciated that in one embodiment, the blocks within a module are each entities in an object-oriented programming paradigm whose inputs and outputs are connected to effectuate communication between the blocks. During execution, the processor processing the module runs each of the blocks in turn at another time, and uses the inputs to the blocks to generate outputs from the blocks, which are then made available to the inputs of other blocks as defined by the specified communication links between the blocks.

At block 94, the operator specifies the particular manipulated, controlled, constrained, and disturbance variables to be used in block 38. If desired, the user can enter a configuration program such as program 40 from 1 , view the control template, select inputs and outputs to be named and configured, browse any standard browsers in the configuration environment to find the current inputs and outputs within the control system, and set these current controls as input and output controls for the control template to select. 5 represents a screen display generated by a configuration program showing a control module DEB_MPC with several interconnected function blocks including several analog input (AI) and analog output (AO) function blocks, several PID control function blocks and an MPC-PRO function block, which is an advanced function block. The tree structure on the left side of the display of 5 represents the function blocks within the DEB_MPC module, which include, for example, block 1, C4_AI, C4_DGEN, etc.

As will become clear, the user can specify the inputs to and outputs from the MPC-PRO function block by drawing lines between these inputs and outputs and the inputs and outputs of other function blocks. Alternatively, the user can select the MPC-PRO block to access the properties of the MPC-PRO block. A dialog box like that of 6 , may be displayed to allow a user to view the properties of the MPC-PRO block. As in 6 , a different table can be provided for each of the control, manipulated, disturbance, and constraint (auxiliary) variables to organize these variables, which is particularly necessary when numerous variables such as 20 or more of each, the advanced Control / regulation block 38 are assigned. Within the table for a certain type of quantity there can be a description, a low and high limit (constraints) and a path designation. In addition, the user or operator can specify what the block should do with the quantity in the event of a failed condition, such as taking no action, using the simulated value of the quantity instead of the measured value, or allowing manual input. In addition, the operator can also specify whether this quantity should be minimized or maximized for optimization and can specify the priority or weight and utility values associated with this quantity. These fields must be filled in if no default objective function is used. Of course, the user can add, move, modify or delete dates or sizes using the appropriate buttons on the right side of the dialog box.

The user can set or change the data of one or more of the sizes by selecting the size. In this case, the user can be presented with a dialog box like that of 7 for the RETURN FLOW manipulated variable (REFLUX FLOW). The user can change the data in their different fields and set data like the pathname of the size (ie their input and output connection) by browsing. Using the screen of 7 the user can select an internal search button or an external search button to search inside the module or outside the module housing the MPC-PRO block. Of course, if desired, the operator or user can manually provide an address, path or tag name, etc. defining the connections to and from the inputs and outputs of the advanced control block, if so desired.

After selecting the inputs and outputs to and from the advanced control function block, the user can define the setpoints associated with the control variables, the ranges or limits associated with the control, auxiliary and manipulated variables, and the limits associated with each of the control, auxiliary - and manipulated variables related weightings. Of course, some of this data, such as constraint limits or ranges, may already be associated with these quantities because these quantities were selected from or found in the process control system configuration environment. If desired, at a block 96 of 3 configure the one or more functions to be used in the optimizer by specifying the unit costs and/or the unit utility for the manipulated variables, control variables or auxiliary variables. Of course, the operator can choose to use the default target function as previously described. 8th is a screen display provided by a configuration program that allows a user or operator to select one of a set of target functions for making an advanced control block. It is understood that the user can see the screen display like the one shown in 8th intended is, to select a set of previously stored objective functions, shown here as default objective function and objective functions 2-5.

After the inputs (control, auxiliary, and disturbance) are named and linked to the advanced control template, and the weights, limits, and setpoints at a block 98 of 3 associated with it, the advanced control template is downloaded to a selected controller within the process as a functional block to be used for control. The general nature of this control block and the way to configure this control block is in the U.S. Patent No. 6,445,963 entitled "Integrated Advanced Control Blocks in Process Control Systems" which is assigned to the assignee of the present application and is expressly incorporated herein by reference. While this patent describes the manner of making an MPC controller in a process control system and does not describe the manner in which an optimizer may be connected to that controller, it is clear that the general steps taken to make the controller connect and configure, may be used for the control block 38 described herein, the template including all of the logical elements for the control block 38 discussed herein, not just those described in this patent.

In any event, at a block 100, after the advanced control template has been downloaded to the controller, the operator can choose to run a test phase of the control template to produce the step response matrix and process model to be used in the MPC controller algorithm. As described in the above referenced patent, during the test phase, the control logic in the advanced control block 38 provides the process with a series of pseudo-random waveforms as manipulated variables and observes the changes in the controlled and auxiliary variables (which the MPC controller essentially uses as control variables are treated). If desired, the manipulated variables and disturbance variables as well as the control and auxiliary variables from the data collection 12 from 1 can be collected and the operator can run the configuration program 40 ( 1 ) to retrieve this data from the data collection 12 and to trend this data in some manner to obtain or determine the step-response matrix, each step-response representing the time response of one of the control or auxiliary quantities to a unit change in one (and only one) of the manipulated and control variables. This unit change is generally a step change, but could also be another type of change such as a pulse or slope change. On the other hand, if desired, the control block 38 can generate the step response matrix in response to the data collected when the pseudo-random waveforms are applied to the process 50, and then makes these waveforms available to the operator interface 13, which can be used by the operator or user is used to manufacture and install the advanced control block 38.

9 represents a screen display that may be provided by the test program to provide the operator with graphical representations of the data collected and trended to enable the operator to guide the creation of the step-response curves and hence the process model or control matrix which to be used in the MPC controller of the advanced control block. In particular, a graph display area 101 displays the data for a plurality of inputs or outputs or other data (as previously specified by the operator) in response to the test waveforms Quantities being analyzed for trend, the name of the quantity, the instantaneous value of the quantity in bar graph form, a target value (indicated by a larger triangle above the bar graph), if any, and limits (indicated by smaller triangles above the bar graph), if any. Other areas of the display present other information about the advanced control block, such as the block's target and current mode of operation (104) and the configured time to steady state (106).

Prior to creating a process model for the advanced control block, the operator can graphically specify the data to be used from trending graphs 101 . In particular, the operator can specify start and end points 108 and 110 of graph 102 as the data to be used to construct the step-response curve. The data in this area may be hatched in a different color, such as green, to visually indicate the selected data. Likewise, the operator can specify areas within the shaded area to be excluded (because they are unrepresentative, an effect of noise or an unwanted disturbance, etc.). This area is shown between lines 112 and 114 and may, for example, be shaded red to indicate that this data was used in the generation of the stepant words should not be included. Of course, the user could include or exclude any data desired and can perform these functions on any of several trend graphs ( 9 shows that in this case eight graphic trend displays are available), with the different graphic trend displays being associated, for example, with different manipulated, control, auxiliary variables, etc.

To create a set of step responses, the operator can click on the on-screen display of 9 select the build model button 116 and the build program uses the data selected from the trend graphs to build a set of step responses, each step response indicating the response of one of the control or auxiliary variables to one of the manipulated or disturbance variables. This manufacturing process is well known and will not be described in more detail here.

Again referring to 3 After the step response matrix (or pulse, slew response matrix, etc.) has been established, in the case where the control and auxiliary variables outnumber the manipulated variables, the step response matrix (or pulse, slew response matrix, etc.) is used to: select the subset of controls and auxiliary quantities to be used in the MPC algorithm as the M by M process model or control matrix to be inverted and used in the MPC controller 52 . This selection process can be done manually by the operator or automatically by a program, for example in the user interface 13 which has access to the step response matrix. Generally speaking, a single one of the control and auxiliary variables is identified as being most closely related to a single one of the manipulated variables. Thus, a single and only (i.e., other) one of the control or auxiliary variables (which are inputs to the process controller) is associated (e.g., paired) with each of the other manipulated variables (which are outputs from the process controller) such that the MPC algorithm can be based on a process model constructed from a set of M by M step responses.

In an embodiment that uses a heuristic approach by providing pairing, the automated program or operator selects the set of M controls and auxiliary variables (where M equals the number of manipulated variables) in an attempt to match the single control or to select an auxiliary variable which has a certain combination of the greatest gain and the fastest response time to a unit change in a single one of the manipulated variables, and to form a pair of these two variables. Of course, in some cases, a single control or auxiliary variable can have high gain and fast response time to multiple manipulated variables. Here, that control or auxiliary variable can be combined with any of the associated manipulated variables to form a pair and can also form a pair with a manipulated variable that does not produce the greatest gain and response time, because in the group the manipulated variable that has the lower gain or slower response time caused, no other control or auxiliary variable can influence to an acceptable extent. Thus, the pairs of manipulated variables, on the one hand, and the control or auxiliary variables, on the other hand, are selected overall in such a way that the manipulated variables are paired with the subset of controlled and auxiliary variables that represent the control variables that are most responsive to the manipulated variables.

The automatic program or the operator can also try to include control and auxiliary variables CV and AV which are uncorrelated, highly correlated, minimally correlated, etc. In addition, it also doesn't matter if not all of the control variables are selected as one of the subset of the M control and auxiliary variables and therefore the MPC controller does not get all of the control variables as inputs to it, because the set of target control and auxiliary variables from the optimizer is selected to represent an operating point of the process where the unselected control variables (as well as the unselected auxiliary variables) are at their set point or within their intended range of operation.

Of course, since there are tens and even hundreds of controls and auxiliary variables on the one hand and tens or hundreds of manipulated variables on the other hand, it can be difficult, at least from a visualization standpoint, to select the set of controls and auxiliary variables that have the best response to the various manipulated variables. To overcome this problem, the advanced control block generator program 40 in the operator workspace 13 may include or present a set of screen displays to the user or operator to allow the operator to make appropriate selections of control and auxiliary variables, which are to be used as a subset of the control and auxiliary variables to be used in the MPC controller 52 during operation.

In this way, with an in 3 Illustrated block 120 the operator are offered a screen mask in which the operator the response of each of the control and auxiliary variables to a can view certain or selected of the manipulated variables. Such a screen mask is in 10 is shown showing the response of each of several control and auxiliary variables (called a constraint) to a manipulated variable called TOP_DRAW. The operator can scroll through the manipulated variables one at a time and view the step responses of each of the control and auxiliary variables to each of the different manipulated variables and during the process select the one control or auxiliary variable that best responds to that manipulated variable. Typically, the operator will attempt to select the control or auxiliary variable that has the best combination of highest steady-state gain and fastest response time to the manipulated variable. As in 11 , one of the control and auxiliary variables can be selected using the dialog box as the most significant for that manipulated variable. If desired, as in 11 displayed, the variable selected from the control and auxiliary variables are highlighted in a different color, such as red (while the previously selected i.e. control and auxiliary variables selected for other manipulated variables are highlighted in a different color, such as yellow be able). In this embodiment, the control program 40, which of course stores the previously selected control and auxiliary variables in memory, can perform a check to ensure that the operator does not select the same control or auxiliary variable as the variable associated with two different manipulated variables. If the user or operator selects a control or auxiliary variable that has already been selected for another manipulated variable, the program 40 can send the user or operator an error message to inform the user or operator about the selection of a previously selected control or auxiliary variable to inform. In this way, the program 40 prevents the selection of the same control or auxiliary variable for two or more different manipulated variables.

As in 12 shown, the operator or user can choose to view the different step responses for each of the different manipulated and disturbance variables. 12 represents the step responses of the TOP_END_POINT to each of the manipulated and disturbance variables previously specified for the advanced control block to be created. Of course, the operator can change the screen mask from 12 Use to select one of the manipulated variables associated with the control variable TOP_END_POINT.

The related to the 10 - 12 The selection procedure described is based on graphical displays, where a control or auxiliary variable can be selected from a display as the most significant. Additionally or alternatively, the data may be presented to the operator in some other way, such as in tabular form, to help complete an M by M controller configuration. An example of a screen display that helps complete an M by M controller configuration and that provides data in tabular form is shown in 13 shown. In this example, the variables available to the control matrix (control CV, auxiliary AV and manipulated variables MV), the condition numbers of the matrix configuration, etc. are provided in tabular form. In a section 204 of the screen display 200 are indications of controls and auxiliary variables that are not already part of a control matrix configuration, along with response parameters associated with the controls and auxiliary variables (e.g., gain, dead time, priority, time constant, etc.) for each of the process variables are listed. In a section 208 an indication of the current configuration of the MPC controller is displayed. A column 212 provides information about available manipulated variables, and a column 216 provides information about output variables (eg, control or auxiliary variables) currently contained in the MPC control matrix.

The display example 200 shows a quadratic controller with the manipulated variables MV TOP_DRAW, SIDE_DRAW and BOT_REFLUX and controller inputs including the control and auxiliary variables BOT_TEMP, SIDE_END_POINT, TOP_END_POINT. When a user selects one of the manipulated variables using an input device such as a mouse, trackball, touch screen, etc., the selected manipulated variable may be highlighted. For example, in the exemplary screen display 200, the manipulated variable TOP_DRAW is highlighted. In addition, response parameters related to the available control and auxiliary variables displayed in section 204 and corresponding to the selected manipulated variable are displayed. For example, gains 220 and dead times 224 are displayed in the exemplary screen display 200, which correspond to the manipulated variable TOP_-DRAW and are assigned to the available control and auxiliary variables 228.

• - Prozessmatrix: eine vollständige N-mal-M-Prozessmatrix mit Steuer- und Randbedingungsgrößen entlang einer ersten Achse und den Stellgrößen entlang einer zweiten Achse;
• - Aktuelle Konfiguration: die M-mal-M-Konfiguration, die aktuell vom Bediener ausgewählt wurde und in der Tabelle „MPC Controller Input-Otput Configuration“ angezeigt wird;
• - Automatische Konfiguration: die automatisch von einem Auswahlprogramm in der MPC-Anwendung ausgewählte M-mal-M-Konfiguration.

Display 200 also includes an add button 232a and a delete button 232b for moving control and auxiliary (constraint) quantities between sections 204 and 208. The figure also shows the condition number of matrix configurations with different gain:

• - process matrix: a complete N by M process matrix with control and constraint variables along a first axis and the manipulated variables along a second axis;
• - Current configuration: the M by M configuration currently selected by the operator and displayed in the MPC Controller Input-Otput Configuration table;
• - Auto Configuration: the M by M configuration automatically selected by a selection program in the MPC application.

The display 200 also includes a button 236 for resetting to a configuration that was automatically determined. Thus, after making changes to an automatic configuration and wishing to return to the automatic configuration, an operator can select button 236 .

Using the data in a display as displayed in display 200 and knowing the process, an operator can build a square matrix in any desired manner.

As will become clear, the screen displays of the 10 - 13 the operator to visualize and select the subset of M controls and auxiliary quantities to be used as inputs to the MPC control algorithm (block 120 of 3 ) can be used, which is particularly useful when there are many of these sizes. Also, the set of control and constraint variables that are determined at block 120 may be selected automatically or electronically based on some predetermined criteria or a selection program. In one embodiment, a selection program may select the inputs based on any combination of response parameters (one or more gains, dead time, priority, time constant, etc.) as determined from the step (or pulse, slope, etc.) responses were for which the control boundary conditions and the manipulated variables are to be used. In another embodiment, a selection program may use some form of time series analysis of the values of the controller's input and output parameters. For example, a cross-correlation between the manipulated and control or auxiliary variables can be used to use the most responsive control or auxiliary variable as the controller input. As another example, cross-correlation between the control and auxiliary quantities can be used to remove colinear (ie, correlated) controller inputs from the matrix. A program can also include any set of heuristics formed from model analysis or process knowledge.

In another embodiment, an automatic selection program can first determine a control matrix by selecting an input/output matrix based on the number of conditions of the matrix, i.e. by minimizing the number of conditions to any desired level, and then building a controller configuration from the control matrix.

In this example, for a process gain matrix A, the constraint number of the matrix ATA can be determined to test the controllability of the matrix. A small number of conditions generally means better controllability, while a larger number of conditions means less controllability and more control steps or larger movements during the dynamic control process. There are no strict criteria to set an acceptable level of controllability, and therefore the number of constraints can be used as a relative comparison of different potential control matrices and as a check for ill-constrained matrices. As is known, a condition number for an ill-conditioned matrix approaches infinity. Mathematically, bad conditionality occurs in the case of collinear process variables, i.e. due to collinear rows or columns in the control matrix. Thus, an important factor affecting constraint count and controllability is cross-correlation between matrix rows and columns. Careful selection of the input/output sizes in the control matrix can reduce constraint problems. In practice it should matter whether the condition count of a control matrix is in the hundreds (e.g. 500) or more. With such a matrix, the movements of the controller manipulated variables can be highly excessive.

As previously explained, the control matrix solves the dynamic control problem while the LP optimizer solves the steady-state optimization problem, and the control matrix must be a square input/output matrix even if the MPC controller block has an unequal number of MVs and CVs (including AVs). To start selecting the inputs and outputs for the control matrix to be used in manufacturing the controller, all available MVs are typically included or selected as controller outputs. After selecting the outputs (the MVs), the process outputs (i.e. the CVs and AVs) that have become part of the dynamic control matrix must be selected to produce a square control matrix that is not ill-conditioned.

A method to automatically or manually select CVs and AVs as inputs to the control matrix will now be discussed, with the understanding that other methods can also be used. This embodiment enhances the robustness of the resulting controller by applying a technique called "MV wrap around" (or self-steering MVs) and automatically applying the penalty factor to motion factors for the MPC controller's MVs appreciates.

Step 1 - The CVs are selected until, if possible, the number of CVs equals the number of MVs (i.e. the number of controller outputs). If there are more CVs than MVs, the CVs can be selected in any order based on any desired criteria such as priority, gain or phase response, user input, correlation, analysis, etc. If the total possible number of CVs equals the number of MVs, go to step 4 to check the resulting constraint number of the square control matrix for acceptability. If the number of CVs is less than the number of MVs, AVs can be selected as described in step 2. If there are no CVs defined, the AV with the highest gain relative to an MV is selected and then go to step 2.

Step 2 - The number of conditions is calculated, one at a time, for each possible AV to be added to the already selected control matrix defined by the previously selected CVs and AVs. As will be appreciated, the matrix defined by the selected CVs contains a row for each selected CV and AV which defines the steady state gain for that CV or AV to each of the previously selected MVs.

Step 3 - The AV set in step 2 is determined which gives the minimum condition count for the resulting matrix and the matrix is set like the previous matrix with the addition of the selected AV. If the number of MVs is now equal to the number of CVs selected, plus the number of AVs selected (i.e. the matrix is now square), then go to step 4. Otherwise, return to step 2.

Step 4 - The constraint number for the constructed square control matrix _Ac is calculated. If desired, the constraint count calculation for matrix A _c can be used instead of matrix A _c ^T A _c because the number of constraints for these different matrices are related as the square root of the other. If the condition count is acceptable, skip steps 5 and 6 and go to step 7.

Step 5 - A wraparound procedure is performed for each of the selected MVs and the constraint count of the matrix resulting from each wraparound procedure is calculated. In essence, a wrap-around procedure can be performed by sequentially substituting a unit response (gain=1.0; deadtime=0, time constant=0) for each of the different MVs for the removed AV (or CV). The unit response will be one at one of the locations in the row of the matrix and zero somewhere else. Essentially, each of the individual MVs is used in this case as an input and an output instead of the AV (or CV) to form a well-conditioned square control matrix. As an example, for a four by four matrix, the combinations 1000, 0100, 0010 and 0001 are placed in the row of the eliminated AV line in gain matrix A _c .

Step 6 - After performing a wraparound procedure for each of the MVs, the combination that gives the minimum condition count is selected. If there is no improvement compared to the condition count obtained in step 4, the original matrix is retained.

Step 7 - At this point, each CV and AV selected is associated with an MV by selecting the CV or AV with the best response (maximum gain, fast response time) relative to a particular MV, excluding the MV used for self-steering (i.e. the MV that has made the round trip). Ensure that each MV is paired with a single CV (or AV) in the case of more than one MV providing high gain and fast response ratio with a single CV (or AV) or vice versa for the CV ( AV) parameters has. The circulated MV is associated with itself. When all parameters have been paired, the selection process is complete.

Of course, the control matrix formed by this procedure, as well as the resulting condition count, can be presented to the user, and the user can accept or reject the formed control matrix for use in manufacturing the controller.

It would be noted that in the automatic procedure described above, at most only one (ie, circulated) MV was selected for self-steering in order to improve steerability. In the manual process, the number of MVs circulated can be arbitrary. For example, with reference to 11 the selection of the most significant CVs or AVs can be canceled again. Regarding 13 CVs or AVs can be removed using the delete button 232b. In these examples, the MVs selected for self-steering are conspicuous by the absence of a corresponding output size selection in the controller configuration. For example 14 the display 200 from 13 in which the manipulated variables TOP_DRAW and SIDE_DRAW do not contain a corresponding control variable CV. In this way, the manipulated variables TOP_DRAW and SIDE_DRAW can be circulated. The display 200 of 13 could on the in 14 can be modified by first selecting the pair BOT_TEMP and TOP_DRAW in the display 200 and then selecting the delete button 232b. Next, the pair SIDE_END_POINT and SIDE_DRAW could be selected in the display 200 and then the delete button 232b selected. In the ad 200 of 14 then the sizes BOT_TEMP and SIDE_END_POINT are placed in column 228 of the available sizes. In addition, the condition count differs from the current, in 14 configuration shown depends on the condition number of the automatic configuration.

Also, more MVs can be used as wrap sizes for control if the number of MVs is greater than the number of total CVs plus AVs. This way, the controller still ends up with a square control matrix with each of the MVs as outputs. It will be appreciated that the method of performing and using wraparound means that the number of CVs and AVs selected for the control matrix can be less than the number of MVs controlled by the controller, with the difference in the wraparound number of MVs as inputs in the control matrix exists. In addition, this round-robin procedure can be used in a process that has fewer CVs plus AVs than MVs.

Of course, the above condition number is calculated using the steady-state gains, and therefore the control matrix essentially defines the controllability for the steady-state as well. Process dynamics (dead time, lag, etc.) and model uncertainty also have an impact on dynamic controllability, and these impacts can be accounted for by changing the priority of process variables (e.g., control and auxiliary variables), which dictates their inclusion in the control matrix due to the impact they have on dynamic control.

It is also possible to use other heuristics designed to improve both steady state and dynamic controllability. Such a method would typically involve a number of heuristic criteria, possibly some that are contradictory, applied in multiple phases to form a control matrix and thereby select an appropriate set of controller inputs that provide certain improvements to the control matrix. In such a heuristic, the CVs and the AVs are grouped by MV based on the highest boost ratio. Then, for each MV grouping, the one process output with the fastest dynamics and significant gain is selected. This selection process may consider a confidence interval and favor CVs over AVs (all else being equal). The process model generator program then uses the parameters selected from each group during manufacture of the MPC controller. Because only one parameter is chosen for each MV, the response matrix is square and can be inverted.

worin DT_i die Totzeit in MPC-Abtastungen für ein MVi-CVj-Paar ist, G_i die Verstärkung (dimensionslos) für das MVi-CVj-Paar ist, und die Paarbildung diejenige ist, die während der quadratischen Controllerkonfiguration aufgebaut wird. Die Quadratmatrixpaarbildung liefert deshalb PM-Werte, die dazu beitragen, die miteinander in Konflikt stehenden Controllerbedürfnisse nach Leistung und Robustheit zu erfüllen.In any event, after selecting the subset of M (or fewer) controls and auxiliary inputs to the MPC controller, a block 124 of 3 from the determined square control matrix, the process model or controller used in the MPC control algorithm 86 of 2 should be used. As is well known, this controller fabrication step is a computationally intensive procedure. Primary tuning factors for controller manufacture are "Penalty on Move" parameters (PM parameters) of the controller manipulated variables. Analysis has shown that dead time is an important factor in calculating PM, while gain naturally has a strong impact on controller movements. The following experimental formula accounts for both dead time and gain when estimating a PM factor that ensures stable and responsive MPC operation for model errors up to 50%: $$P{M}_i=3\left(1+\frac{6D{T}_i}{p}+\frac{3G_{i}D{T}_i}{p}\right)$$

where DT _i is the dead time in MPC samples for an MVi-CVj pair, G _i is the gain (dimensionless) for the MVi-CVj pair, and the pairing is that established during the quadratic controller configuration. Square matrix pairing therefore provides PM values that help to meet the conflicting controller needs of performance and robustness.

A block 126 then downloads this MPC process model (with the inherent control matrix) or the controller and if necessary the step responses and the steady state step response gain matrix to the control block 38 and this data is incorporated into the control block 38 for operation. At this point, the control block 38 is ready for on-line operation as part of the process 50.

If desired, the process step responses can be reconfigured or provided in a different way than when these step responses were generated. For example, one of the step responses can be copied from different models and inserted into the screen masks, for example the 10 - 12 be set to determine the step responses of a specific control or auxiliary variable to a manipulated variable or disturbance variable. 15 represents a screen display where the user can select and copy one of the step responses of a particular process or model, and then drag or paste that same response into another model and paste that step response into the new model, thereby allowing the user to manually to specify a step response model. Of course, as part of this process, the user may delete one or more of the automatically generated step response models as previously described.

16 represents a screen display where the user can look at one of the step responses in particular (here for the step response TOP_END_POINT as opposed to TOP_DRAW). The parameters for this step response such as steady state gain, response time, premier time constant and square error are presented on the display for the user or operator's convenience. If desired, the user can view or change the characteristics of this step response by specifying other parameters, such as a different gain or time constant, if so desired. If the user specifies a different gain or other parameter, the step response model can be mathematically regenerated to include that new parameter or set of parameters. This operation is useful when the user knows the parameters of the step response and needs to modify the generated step response to match or satisfy those parameters. Changes to the step response model are in turn reflected in the pairing and fabrication of the square control matrix since the gain and response dynamics are used.

Now are referring to 4 Illustrated are the general steps taken during each cycle of operation or scan of the advanced control block 38, as determined using the flow chart 90a of FIG 3 manufactured, can be performed while the process 50 is running online. At a block 150, the MPC controller 52 receives and processes ( 2 ) the measured values of the control and auxiliary variables CV and AV. In particular, the control prediction process model processes the CV, AV and DV measurements or inputs to produce the future control parameter vector as well as the predicted steady state controls and auxiliary variables CV _SS and AV _SS .

Next, at a block 152, the input processing/filtering block 58 processes or filters ( 2 ) the predicted control, auxiliary and manipulated variables CV _SS , AV _SS and MV _SS formed by the MPC controller 52 and makes these filtered values available to the optimizer 54 . At a block 154, the optimizer 54 performs standard LP procedures to provide the set of M target manipulated variables MVT that maximize or minimize the selected or standard objective function while not violating any of the limits of the auxiliary and manipulated variables and while the controlled variables are at their specified target value or within the specified ranges for these quantities. Generally speaking, the optimizer 54 computes a target manipulated variable solution MV _T by forcing the control and auxiliary variables within their respective limits. As stated above, in many cases a solution will exist where the control variables are each at their setpoint (which is initially treated as an upper limit placed on the control variable), while the auxiliary variables are each within their respective constraint limits. If that is the case, the optimizer 54 only has to output the specific target manipulated variables MVT that yield an optimal result for the target function.

However, in some cases, due to boundary conditions that are too tight for some or all of the auxiliary or manipulated variables, it may not be possible to find an operating point where all control variables are at their setpoint and all auxiliary variables are within their respective constraint limits, because such a solution does not exist. In these cases, as previously noted, the optimizer 54 may allow the specified setpoint ranges of the control variables to be relaxed in order to attempt to find an operating point where the auxiliary variables operate within their respective limits. In this case, if there is no solution, the optimizer can drop one or more of the auxiliary constraint limits as a bound within the solution and/or drop the control variable setpoint range within the solution and instead determine the optimal process operation point and the dropped auxiliary constraints limits and/or the Ignore dropped control variable setpoint range. Here the optimizer chooses which auxiliary or control to drop based on the respective weights assigned to the controls and auxiliary respectively (e.g. lowest weight or highest priority being dropped first). The optimizer 54 continues to drop auxiliary or control variables based on their intended weights and priorities until it finds a target manipulated variable (MV _T ) solution where, for the remaining higher priority controls or auxiliary variables, all setpoint ranges for the Control variables and the limits for the auxiliary variables are observed.

Next, at a block 156, the goal translation block 55 ( 2 ) the steady state step response gain matrix to determine the target values of the control and auxiliary variables CV _T and AV _T from the target values for the manipulated variables MV _T and provides the selected N (where N is less than or equal to M) subset of these values to the MPC controller 52 available as destination entries. At a block 158, the MPC controller 52 uses the control matrix or logic derived therefrom to operate as an unconstrained MPC controller, as previously described, to determine the future CV and AV vector for these target values, performs vector subtraction with the future control parameter vector to produce the future error vector. The MPC algorithm operates in a known manner to determine the steady state manipulated variable MV _SS based on the process model formed from the M by M step responses and provides these MV _SS values to the input processing/filtering block 58 ( 2 ) to disposal. At a block 160, the MPC algorithm also determines the MV steps to be output to the process 50 and outputs the first of those steps to the process 50 in any appropriate manner.

During operation, one or more monitoring applications are running, for example, in one of the workstations 13, data can be retrieved from the advanced control block or other functional blocks connected thereto, either directly or indirectly via the data collection 12, and the user or operator has one or more views - or diagnostic screens are provided to indicate the operational status of the advanced control block. The function block technology has cascade inputs (CAS_IN) and cascade remote inputs (RCAS_IN) and corresponding backcalculation outputs (BCAL_OUT and RCAS_OUT) on both the control and output function blocks. Using these links, it is possible to overlay an optimized MPC supervisory control strategy over the existing control strategy, and this supervised control strategy can be presented using one or more view screens or displays. Likewise, targets for the optimized MPC controller can also be modified from a strategy if so desired.

17 Figure 1 is an example screen display that may be generated by one or more view applications that present an optimizer dialog screen that provides the operator with data related to the operation of the advanced control block during its execution. In particular, the inputs to the process (the manipulated variables MV) and the outputs (the control and auxiliary variables CV and AV) are shown separately. For each of these quantities, the on-screen display provides the name (descriptor) of the quantity, the current value as measured, a target value, if any, the target value as calculated by the optimizer, the units and unit values of the change in size, and an indication of the current size values For the output sizes, there is also an indication of whether this size is one of the selected sizes used in the MPC controller, the predicted value of this size as determined by the MPC controller, and the default priority for this size. This screen allows the operator to view the current operating state of the advanced control block and the manner in which the advanced control block is performing control. In addition, the user can configure some control parameters for remote setpoint capability, allowing external applications to set operational targets for throughput coordination.

18 is a screen that can be generated by a diagnostics application that presents a diagnostics screen that can be provided to a user or operator to diagnose the advanced control block. In particular, the diagnostic screen of 18 the control and boundary condition (auxiliary) variables, the manipulated variables and the disturbance variables separately. For each, the name or descriptor is provided along with an indication (in the first column) whether an error or warning condition exists for that entity. Such an error or warning may be displayed graphically using, for example, a green tick or a red "x" or in any other desired manner. A value or status is also given for each of these quantities. For the manipulated variables, the value and status of the Back_Cal variable (recalculated or returned variable) for these signals is shown. As will be appreciated, this screen can be used to diagnose the advanced control block, providing the operator with the data necessary to diagnose problems within the control system. Of course, other types of screens and data may also be provided to the operator to enable him to view and diagnose the operation of the advanced control block and the module in which it is implemented.

While the advanced control block is shown here as comprising an optimizer located in the same functional block and therefore implemented in the same device as the MPC controller, it is also possible to house the optimizer in a separate device. In particular, the optimizer can be housed in another device such as one of the user workstations 13 and connected to the MPC controller as in connection with FIG 2 communicate during each execution or sampling to calculate and provide to the MPC controller the target manipulated variables (MV _T ) or the subset of the control and auxiliary variables (CV and AV) calculated therefrom. Of course, a special interface such as a known OPC interface can be used to provide the communication interface between the controller and the functional block containing the MPC controller and the workstation or other computer that has or is running the optimizer. As in with regard to 2 described embodiment, the optimizer and the MPC controller must still communicate with each other during each scan cycle to perform integrated, optimized MPC control. In this case, however, other desired types of optimizers can also be used, such as known or real-time optimizers that may already exist in a process control environment. This feature can also be used to advantage when the optimization problem is nonlinear and the solution requires nonlinear programming techniques.

Of course, while the advanced control block and other blocks and programs described herein have been described as being used in conjunction with Fieldbus and standard 4-20 ma devices, they can also be implemented using any other process control communication protocol or programming environment and can be used with any other type of device, function block or controller. Although the advanced control blocks and associated generation and test routines described herein are preferably implemented in software, they may also be implemented in hardware, firmware, etc., and may be executed by any other processor associated with a process control system. Thus, the program 40 described herein can be implemented in a standard general purpose CPU or in specially designed hardware or firmware, such as ASICs, if so desired. When implemented in software, the software may be stored in any computer-readable memory such as magnetic disk, laser disk, optical disk or other storage medium, in a computer's or processor's RAM or ROM, etc. Likewise, this software may be delivered to a user or process control system via any known or desired delivery method, such as on a computer-readable disk or any portable computer storage mechanism, or may be modulated over a communications channel such as a telephone line, the Internet, etc. (which may be equivalent or interchangeable with the provision of such software via a portable storage medium).

Reference List

11

10 Process Control System Process Controller

12

data collection

13

Main workstation or computer

14

display screen

15 - 22

field devices

26, 28

Input/Output Cards, I/O Cards

29

communication network

30

Extended block

32, 34

programs, routines

36

control circuit

38

rule/control/rule block

40

Advanced control block generator program, configuration program

42

Control block specification program

44

process modeling program

46

Control logic parameter setting program

48

optimizer

50

process

51

MPC controller block

52

MPC controller block

54

optimizer

55

goal implementation block

56

Step response model, control matrix

58

Input processing/filter block

62

selection block

64

target functions

66

input

70

prediction control process model

72

output

74

Vector Summer

76

input

80

Target control/rule block

82

trajectory filter

84

Vector Summer

86

MPC control algorithm

90

flowchart

90a

Functions before process operation

90b

Functions during process operation

92, 94

block

98

block

100

block

101

graphics display area

102

bar chart area

104

Block target and current operating mode

106

Block time to steady state

108, 110

Beginning, end point of 102

112, 114

lines

116

model making button

120, 124, 126, 150, 152, 154, 156, 158, 160

block

200

screen display

204, 208

Section of 200

212

Column available variables

216

output sizes

220

amplification factors

224

dead times

228

Available control and auxiliary variables

232a

add button

232b

delete key

236

button, reset button

AV

auxiliary quantities

AVSS

prediction steady state auxiliary quantities

AVSSfil

Filtered auxiliary variable

AVT

target assists

CV

control variables

CVSS

prediction steady state controls

CVSSfil

Filtered control variable

CVT

target control variables

dv

disturbances

LP

linear programming

MV

variables

MVSS

prediction steady state manipulated variables

MVSSfil

Filtered manipulated variable

OF

objective function

Further advantageous embodiments result as follows:

1st embodiment

• ein computerlesbares Medium:
  • eine Konfigurationsroutine, die auf dem computerlesbaren Medium gespeichert und dazu ausgelegt ist, auf einem Prozessor abzulaufen, wobei die Konfigurationsroutine Folgendes enthält:
    • eine Speicherroutine, die Informationen speichert, welche mehrere Steuer- und Hilfsgrößen und mehrere Stellgrößen betrifft, welche vom Optimierer und/oder der Mehrfacheingabe-/Mehrfachausgabe-Steuerroutine verwendet werden, wobei die Informationen, die die mehreren Steuer- und Hilfsgrößen und die mehreren Stellgrößen betreffen, Antwortinformationen für jede von mindestens einigen der Steuer- und Hilfsgrößen umfassen, die Antwortinformationen von jeweiligen Antworten für jede von den mindestens einigen der Steuer- und Hilfsgrößen auf jeweilige Stellgrößen anzeigen; und
    • eine Anzeigeroutine, die dazu ausgelegt ist, einem Benutzer eine Anzeige hinsichtlich einer oder mehrerer der Steuer-, Hilfs- und Stellgrößen zu bieten, wobei die Anzeige eine Teilmenge der Antwortinformationen umfasst, wobei die Teilmenge der Antwortinformation Antwortinformationen umfasst, die Antworten von jeder der mindestens einigen der Steuer- und Hilfsgrößen auf mindestens eine der Stellgrößen anzeigen.

A process control configuration system for use in creating or reviewing a control block with an integrated optimizer and a multiple input/multiple output handler, the process control system comprising:

• a computer-readable medium:
  • a configuration routine stored on the computer-readable medium and adapted to run on a processor, the configuration routine including:
    • a storage routine that stores information pertaining to a plurality of control and auxiliary variables and a plurality of manipulated variables used by the optimizer and/or the multiple input/multiple output control routine, the information pertaining to the plurality of control and auxiliary variables and the plurality of manipulated variables , comprise response information for each of at least some of the control and auxiliary variables indicating response information of respective responses for each of the at least some of the control and auxiliary variables to respective manipulated variables; and
    • a display routine configured to provide a user with a display of one or more of the control, auxiliary, and manipulated variables, the display including a subset of the response information, the subset of the response information including response information representing responses from each of the at least display some of the control and auxiliary variables on at least one of the manipulated variables.

2nd embodiment

The process control configuration system according to embodiment 1, wherein the configuration routine further includes a first routine for allowing the user to select the one of the plurality of manipulated variables.

3rd embodiment

The process control configuration system of embodiment 2, wherein the configuration routine further includes a second routine to allow the user to associate the one of the manipulated variables with one of the control and auxiliary variables.

4th embodiment

The process control configuration system of embodiment 3, wherein the configuration routine further includes a third routine for allowing the user to unassign one of the control and auxiliary variables that has been associated with the one of the manipulated variables.

5th embodiment

Process control configuration system according to one of the embodiments 1 to 4,
wherein the display routine is configured to display indications of the plurality of manipulated variables and, for each manipulated variable, to display an indication of the associated plurality of control and auxiliary variables, if any.

6th embodiment

Process control configuration system according to one of the embodiments 1 to 5,
wherein the display routine is configured to display an indication of a current configuration condition number associated with a configuration corresponding to the currently associated manipulated and control and auxiliary variables.

7th embodiment

The process control configuration system according to any one of embodiments 1 to 6, wherein the display routine is adapted to display an indication of an automatic configuration condition number associated with an automatically generated configuration.

8th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the display routine is configured to display an indication of a process matrix configuration condition number associated with a configuration corresponding to a process matrix, all of the plurality of control and auxiliary variables along a first axis of the process matrix and all of the manipulated variables along a second axis of the process matrix having.

9th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the display routine is designed to display indications of available variables of the plurality of control and auxiliary variables, with the available variables being associated with manipulated variables.

10th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the display routine is adapted to display response information indications for each of the available variables, the response information indicating responses from each of the available variables to the one of the manipulated variables.

11th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the response information for each of the available variables contains at least an indication of a gain, an indication of a dead time, an indication of a priority and an indication of a time constant.

12th embodiment

Process control configuration system according to embodiment 11, wherein the indication of the gain comprises a number.

13th embodiment

Process control configuration system according to embodiment 11 or 12, wherein the indication of the dead time comprises a number.

14th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the response information for each of the at least some of the control and auxiliary variables includes at least an indication of a gain, an indication of a dead time, an indication of a priority, and an indication of a time constant.

15th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the response information for each of the at least some of the control and auxiliary variables includes information associated with a step response.

16th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the response information for each of the at least some of the control and auxiliary variables includes data associated with a pulse response.

17th embodiment

Process control configuration system according to one of the preceding embodiments,
wherein the response information for each of the at least some of the control and auxiliary quantities includes information associated with a ramp response.

18th embodiment

A process control system for controlling a process, comprising: a multiple input/multiple output controller configured to generate multiple control outputs during each cycle of operation of the process control system, configured to control the process based on measured multiple inputs from the process and control based on a set of target values provided to the multiple input/multiple output controller during each cycle of operation of the process control system; and
an optimizer configured to form the set of target values for use by the multiple input/multiple output controller during each cycle of operation of the process control system;
where the optimizer is a linear or quadratic programming optimizer including a target function, and the optimizer is designed to minimize or maximize the objective function while maintaining a set of control variables within predetermined setpoint limits, a set of auxiliary variables within a set of predetermined auxiliary variable limits, and
maintaining a set of manipulated variables within a set of predetermined manipulated variable limits and, if no solution exists, allowing at least one of the setpoint limits to be violated.

19th embodiment

process control system according to embodiment 18,
wherein the optimizer is configured to store a set of priorities corresponding to the set of control variables and wherein the optimizer uses the priorities from the set to determine the at least one of the control setpoint limits to violate.

20th embodiment

Process control system according to embodiment 18 or 19,
in which the optimizer is designed, if no solution exists, to allow at least one of the setpoint limits and the auxiliary quantity limits to be violated.

21st embodiment

Process control system according to any one of embodiments 18 to 20, in which the optimizer is arranged to store a first set of priorities corresponding to the set of control variables and a second set of priorities corresponding to the set of auxiliary variables, and in which the The optimizer uses the priorities from the first set and the priorities from the second set to determine the at least one of the control setpoint limits and the auxiliary quantity limits to be violated.

22nd embodiment

• eine Antwortmatrix, die eine Reaktion jeder eines Satzes von Steuer- und Hilfsgrößen auf eine Veränderung in jedem eines Satzes von Stellgrößen definiert, bei dem eine Anzahl von Steuer- und Hilfsgrößen in dem Satz von Steuer- und Hilfsgrößen gleich einer ersten Zahl ist, bei dem eine Anzahl von Stellgrößen in dem Satz von Stellgrößen gleich einer zweiten Zahl ist;
• einen linearen oder quadratischen Optimierer, der dazu ausgelegt ist:
• einen Satz von Zielstellgrößenwerten herzustellen, wobei die Zielstellgrößenwerte einen optimalen Betriebspunkt basierend auf einem Satz von Vorhersagewerten von Steuer- und Hilfsgrößen des Prozesses und basierend auf einem Satz vom aktuellen Werten von Stellgrößen des Prozesses definieren, bei dem eine Anzahl von Vorhersagewerten von Steuer- und Hilfsgrößen in dem Satz von Vorhersagewerten von Steuer- und Hilfsgrößen gleich der ersten Zahl ist, bei dem eine Anzahl von aktuellen Werten von Stellgrößen in dem Satz von aktuellen Werten von Stellgrößen gleich der zweiten Zahl ist;
• einen Satz von Vorhersagesteuergrößen und -hilfsgrößen, einen Satz von Vorhersagestellgrößen und die Antwortmatrix zu verwenden, um einen Satz von Zielwerten für eine vorbestimmte Teilmenge eines Satzes von Steuer- und Hilfsgrößen herzustellen, bei dem eine Anzahl von Vorhersagesteuergrößen und -hilfsgrößen in dem Satz von Vorhersagesteuergrößen und -hilfsgrößen gleich der ersten Zahl ist, bei dem eine Anzahl von Steuer- und Hilfsgrößen in der vorbestimmten Teilmenge des Satzes von Steuer- und Hilfsgrößen sich von der ersten Zahl unterscheidet;
• bei dem der lineare oder quadratische Optimierer dazu ausgelegt ist, den Satz von Zielstellgrößenwerten herzustellen, die eine Zielfunktion maximieren oder minimieren, während jede der Steuergrößen auf ihren vorbestimmten Sollwerten und jede der Hilfsgrößen und Stellgrößen innerhalb vorbestimmter Zwangsgrenzen gehalten werden;
• bei dem der Optimierer dazu ausgelegt ist, den Satz von Zielstellgrößenwerten herzustellen, die die Zielfunktion maximieren oder minimieren, während jede der Steuergrößen innerhalb vorbestimmter Sollwertgrenzen und jede der Hilfsgrößen und Stellgrößen innerhalb von Zwangsgrenzen gehalten werden, wenn keine Lösung besteht, die jede der Steuergrößen auf vorbestimmten Sollwerten und jede der Hilfsgrößen und Stellgrößen innerhalb vorbestimmter Zwangsgrenzen hält;
• bei dem der Optimierer dazu ausgelegt ist, den Satz von Zielstellgrößenwerten herzustellen, die die Zielfunktion maximieren oder minimieren, während jede der Hilfsgrößen innerhalb vorbestimmter Zwangsgrenzen und die Stellgrößen innerhalb vorbestimmter Zwangsgrenzen gehalten werden, während eine oder mehrere der Steuergrößen vorbestimmte Sollwertgrenzen basierend auf Prioritäten verletzen dürfen, die den Steuergrößen zugeordnet sind, wenn keine Lösung besteht, die jede der Steuergrößen innerhalb vorbestimmter Sollwertgrenzen und jede der Hilfsgrößen und Stellgrößen innerhalb vorbestimmter Zwangsgrenzen hält;
• einen Mehrfacheingabe-/Mehrfachausgabe-Controller, der dazu ausgelegt ist:
  • den Satz von Vorhersagesteuergrößen und -hilfsgrößen und den Satz von Vorhersagestellgrößen herzustellen, und
  • den Satz von Zielwerten für die vorbestimmte Teilmenge des Satzes von Steuer- und Hilfsgrößen mit Messwerten der vorbestimmten Teilmenge des Satzes von Steuer- und Hilfsgrößen zu kombinieren, um einen Satz von Stellsteuersignalen zur Steuerung der Stellgrößen des Prozesses herzustellen, bei dem eine Anzahl der Stellsteuersignale im Satz von Stellgrößen gleich der zweiten Zahl ist.

A process control system for controlling a process, comprising:

• a response matrix defining a response of each of a set of control and auxiliary variables to a change in each of a set of manipulated variables, in which a number of control and auxiliary variables in the set of control and auxiliary variables is equal to a first number, in which a number of manipulated variables in the set of manipulated variables equals a second number;
• a linear or quadratic optimizer designed to:
• to produce a set of target manipulated variable values, the target manipulated variable values defining an optimal operating point based on a set of predicted values of control and auxiliary variables of the process and based on a set of current values of manipulated variables of the process, in which a number of predicted values of control and auxiliary variables in the set of predicted values of control and auxiliary variables is equal to the first number, in which a number of current values of manipulated variables in the set of current values of manipulated variables is equal to the second number;
• using a set of prediction controls and auxiliary variables, a set of prediction manipulated variables and the response matrix to produce a set of target values for a predetermined subset of a set of control and auxiliary variables, in which a number of prediction control variables and auxiliary variables in the set of prediction control variables and -auxiliaries equal to the first number in which a number of control and auxiliary variables in the predetermined subset of the set of control and auxiliary variables differs from the first number;
• wherein the linear or quadratic optimizer is adapted to produce the set of target manipulated variable values that maximize or minimize an objective function while maintaining each of the control variables at their predetermined setpoints and each of the auxiliary and manipulated variables within predetermined constraint limits;
• in which the optimizer is designed to produce the set of target manipulated variable values that maximize or minimize the objective function while maintaining each of the controlled variables within predetermined set-point limits and each of the auxiliary and manipulated variables within constraint limits when no solution exists that satisfies each of the controlled variables predetermined setpoints and each of the auxiliary and manipulated variables within predetermined constraint limits;
• wherein the optimizer is designed to produce the set of target manipulated variable values that maximize or minimize the objective function while maintaining each of the auxiliary variables within predetermined constraint limits and the manipulated variables within predetermined constraint limits while allowing one or more of the control variables to violate predetermined setpoint limits based on priorities associated with the controlled variables when there is no solution keeping each of the controlled variables within predetermined setpoint limits and each of the auxiliary and manipulated variables within predetermined constraint limits;
• a multiple input/multiple output controller designed to:
  • to produce the set of prediction control and auxiliary variables and the set of prediction manipulated variables, and
  • to combine the set of target values for the predetermined subset of the set of control and auxiliary variables with measured values of the predetermined subset of the set of control and auxiliary variables to produce a set of positioning control signals for controlling the manipulated variables of the process in which a number of the positioning control signals im set of manipulated variables is equal to the second number.

23rd embodiment

process control system according to embodiment 22,
wherein the optimizer is configured to produce the set of target manipulated variable values that maximize or minimize the objective function while maintaining the manipulated variables within predetermined constraint limits while one or more of the control variables have predetermined set point limits and the auxiliary variables have predetermined constraint limits based on priorities affecting the Control variables and associated auxiliary variables may violate if there is no solution that keeps each of the auxiliary variables and manipulated variables within predetermined constraint limits.

24th embodiment

Process control system according to embodiment 22 or 23,
wherein the optimizer is configured to produce the set of target manipulated variable values that maximize or minimize the objective function while maintaining the manipulated variables within predetermined constraint limits while one or more of the control variables have predetermined set point limits and the auxiliary variables have predetermined constraint limits based on priorities affecting the Control variables and the auxiliary variables are assigned, may violate if there is no solution that keeps each of the control variables within predetermined setpoint limits and the manipulated variables within predetermined forced limits.

25th embodiment

• eine Teilmenge der vielen Steuer- und Hilfsgrößen auszuwählen, die beim Durchführen der Prozesssteuerung verwendet werden sollen, bei dem das Auswählen der Teilmenge umfasst, eine der Steuer- oder Hilfsgrößen auszuwählen, die am stärksten auf eine der Stellgrößen anspricht;
• eine Steuermatrix unter Verwendung der ausgewählten Teilmenge der vielen der Steuer- und Hilfsgrößen und der mehreren Stellgrößen herzustellen;
• einen Controller aus der Steuermatrix mit der ausgewählten Teilmenge der vielen der Steuer- und Hilfsgrößen als Eingaben und den mehreren Stellgrößen als Ausgaben zu generieren;
• eine Prozessoptimierung durchzuführen, indem ein Prozessbetriebspunkt ausgewählt wird, um eine Zielfunktion in Abhängigkeit von den mehreren Stellgrößen und den vielen Steuer- und Hilfsgrößen zu minimieren oder zu maximieren, wobei der Prozessbetriebspunkt durch einen Satz von Zielwerten für die ausgewählte Teilmenge der vielen Steuer- und Hilfsgrößen definiert wird;
• ein Mehrfacheingabe-/Mehrfachausgabe-Steuerverfahren unter Verwendung eines Controllers durchzuführen, der aus der Steuermatrix generiert wurde, um einen Satz von Stellgrößenwerten aus den Zielgrößen für die ausgewählte Teilmenge der vielen Steuer- und Hilfsgrößen und Messwerte der ausgewählten Teilmenge der vielen Steuer- und Hilfsgrößen zu bilden; und
• den gebildeten Satz von Stellgrößenwerten zur Steuerung des Prozesses zu verwenden.

A method of controlling a process with multiple manipulated variables and many control and auxiliary variables that can be affected by changes in the manipulated variables, in which the plurality of manipulated variables differ in number from the multiple control and auxiliary variables, the method comprising:

• select a subset of the plurality of control and auxiliary variables to be used in performing process control, wherein selecting the subset comprises selecting one of the control or auxiliary variables that is most responsive to one of the manipulated variables;
• construct a control matrix using the selected subset of the plurality of the control and auxiliary variables and the plurality of manipulated variables;
• generate a controller from the control matrix with the selected subset of the plurality of the control and auxiliary variables as inputs and the plurality of manipulated variables as outputs;
• perform a process optimization by selecting a process operating point to minimize or maximize a target function depending on the multiple manipulated variables and the many control and auxiliary variables, the process operating point by a set of target values for the selected subset of the many control and auxiliary variables is defined;
• perform a multiple-input/multiple-output control method using a controller generated from the control matrix to assign a set of manipulated variable values from the target variables for the selected subset of the multiple control and auxiliary variables and measured values of the selected subset of the multiple control and auxiliary variables form; and
• to use the formed set of manipulated variable values to control the process.

26th embodiment

method according to embodiment 25,
wherein selecting one of the controls or auxiliary variables as most responsive to one of the manipulated variables comprises selecting one of the control or auxiliary variables based at least on a cross-correlation analysis.

27th embodiment

Method according to embodiment 25 or 26,
wherein selecting one of the control or auxiliary variables as most responsive to one of the manipulated variables comprises selecting one of the control or auxiliary variables based on at least one heuristic method.

28th embodiment

Method according to any one of embodiments 25 to 27,
wherein selecting one of the controls or auxiliary variables as most responsive to one of the manipulated variables comprises selecting one of the control or auxiliary variables based at least on priorities associated with the control and auxiliary variables.

29th embodiment

• eine Teilmenge der vielen Steuer- und Hilfsgrößen auszuwählen, die beim Durchführen der Prozesssteuerung verwendet werden sollen, bei der eine Anzahl von Steuer- und Hilfsgrößen in der Teilmenge geringer ist als eine Anzahl von Stellgrößen bei den mehreren Stellgrößen;
• eine Steuermatrix unter Verwendung der ausgewählten Teilmenge der vielen der Steuer- und Hilfsgrößen und der mehreren Stellgrößen herzustellen;
• einen Controller aus der Steuermatrix mit der ausgewählten Teilmenge der vielen der Steuer- und Hilfsgrößen als Eingaben und den mehreren Stellgrößen als Ausgaben zu generieren;
• eine Prozessoptimierung durchzuführen, indem ein Prozessbetriebspunkt ausgewählt wird, um eine Zielfunktion in Abhängigkeit von den mehreren Stellgrößen und den vielen Steuer- und Hilfsgrößen zu minimieren oder zu maximieren, wobei der Prozessbetriebspunkt durch einen Satz von Zielwerten für die ausgewählte Teilmenge der vielen Steuer- und Hilfsgrößen definiert wird;
• ein Mehrfacheingabe-/Mehrfachausgabe-Steuerverfahren unter Verwendung eines Controllers durchzuführen, der aus der Steuermatrix generiert wurde, um einen Satz von Stellgrößenwerten aus den Zielgrößen für die ausgewählte Teilmenge der vielen Steuer- und Hilfsgrößen und Messwerte der ausgewählten Teilmenge der vielen Steuer- und Hilfsgrößen auszubilden; und
• den ausgebildeten Satz von Stellgrößenwerten zur Steuerung des Prozesses zu verwenden.

A method of controlling a process having multiple manipulated variables and many control and auxiliary variables that can be affected by changes in the manipulated variables, in which the plurality of manipulated variables differ in number from the multiple control and auxiliary variables, the method comprising:

• select a subset of the plurality of control and auxiliary variables to be used in performing the process control in which a number of control and auxiliary variables in the subset is less than a number of manipulated variables in the plurality of manipulated variables;
• construct a control matrix using the selected subset of the plurality of the control and auxiliary variables and the plurality of manipulated variables;
• generate a controller from the control matrix with the selected subset of the plurality of the control and auxiliary variables as inputs and the plurality of manipulated variables as outputs;
• perform a process optimization by selecting a process operating point to minimize or maximize a target function depending on the multiple manipulated variables and the many control and auxiliary variables, the process operating point by a set of target values for the selected subset of the many control and auxiliary variables is defined;
• perform a multiple-input/multiple-output control method using a controller generated from the control matrix to form a set of manipulated variable values from the target variables for the selected subset of the multiple control and auxiliary variables and measured values of the selected subset of the multiple control and auxiliary variables ; and
• use the trained set of manipulated variable values to control the process.

30th embodiment

• ein computerlesbares Medium;
• einen Funktionsblock, der auf dem computerlesbaren Medium gespeichert und dazu ausgelegt ist, auf dem Prozessor abzulaufen, um während jeder Steuerabtastperiode eine Mehrfacheingabe-/Mehrfachausgabesteuerung des Prozesses zu bewerkstelligen, wobei der Funktionsblock umfasst:
  • eine Zielfunktion, die ein Optimierungskriterium basierend auf den mehreren Steuer- und Hilfsparametern definiert, bei dem die Zielfunktion ein Optimierungskriterium basierend auf einer ersten Anzahl von Steuer- und Hilfsparametern definiert;
  • ein Optimierungsprogramm, das die Zielfunktion verwendet, um während jeder Steuerabtastperiode einen Satz von optimalen Zielwerten für die Steuer- und Hilfsparameter herzustellen, bei dem das Optimierungsprogramm eine lineare oder quadratische Programmierroutine enthält;
  • eine Steuermatrix, die eine vorbestimmte Teilmenge der mehreren der Steuer- und Hilfsparameter zu den vielen Stellparametern in Beziehung setzt, bei dem eine Anzahl von Steuer- und Hilfsparametern bei der vorbestimmten Teilmenge gleich der ersten Zahl ist, bei dem eine Anzahl von Stellparametern bei den vielen Stellparametern gleich der ersten Zahl ist; und
  • ein Mehrfacheingabe-/Mehrfachausgabe-Steuerprogramm, das während jeder Steuerabtastperiode ein Steuersignal für jeden der vielen Stellparameter unter Verwendung der Steuermatrix und der Zielwerte für die Teilmenge der mehreren Steuer- und Hilfsgrößen erzeugt, bei dem die Steuersignale dazu bestimmt sind, die Teilmenge der mehreren der Steuer- und Hilfsparameter auf die für die Teilmenge von Steuer- und Hilfsparametern optimalen Zielwerte zu fahren, bei dem das Mehrfacheingabe-/Mehrfachausgabe-Steuerprogramm ein Modellvorhersagesteuerprogramm umfasst.

A process control element adapted to be used as part of a process control program implemented on a processor to control multiple control and auxiliary parameters of a process using multiple manipulated parameters, the process control element comprising:

• a computer-readable medium;
• a function block stored on the computer readable medium and adapted to run on the processor to effectuate multiple input/multiple output control of the process during each control sampling period, the functional block comprising:
  • an objective function defining an optimization criterion based on the plurality of control and auxiliary parameters, wherein the objective function defines an optimization criterion based on a first number of control and auxiliary parameters;
  • an optimization program using the objective function to produce a set of optimal target values for the control and auxiliary parameters during each control sample period, wherein the optimization program includes a linear or quadratic programming routine;
  • a control matrix relating a predetermined subset of the plurality of control and auxiliary parameters to the plurality of adjustment parameters, in which a number of control and auxiliary parameters in the predetermined subset is equal to the first number, in which a number of adjustment parameters in the plurality setting parameters is equal to the first number; and
  • a multiple-input/multiple-output control program that generates a control signal for each of the plurality of control parameters during each control sample period using the control matrix and the target values for the subset of the plurality of control and auxiliary variables, wherein the control signals are designed to control the subset of the plurality of the drive control and auxiliary parameters to the optimal target values for the subset of control and auxiliary parameters, wherein the multiple-input/multiple-output handler comprises a model prediction handler.

31st embodiment

• ein computerlesbares Medium;
• einen Funktionsblock, der auf dem computerlesbaren Medium gespeichert und dazu ausgelegt ist, auf dem Prozessor abzulaufen, um während jeder Steuerabtastperiode eine Mehrfacheingabe-/Mehrfachausgabesteuerung des Prozesses zu bewerkstelligen, wobei der Funktionsblock umfasst:
  • eine Zielfunktion, die ein Optimierungskriterium basierend auf den mehreren Steuer- und Hilfsparametern definiert;
  • ein Optimierungsprogramm, das die Zielfunktion verwendet, um während jeder Steuerabtastperiode einen Satz von optimalen Zielwerten für die Steuer- und Hilfsparameter herzustellen;
  • eine Steuermatrix, die eine vorbestimmte Teilmenge der mehreren der Steuer- und Hilfsparameter zu den vielen Stellparametern in Beziehung setzt; und
  • ein Mehrfacheingabe-/Mehrfachausgabe-Steuerprogramm, das während jeder Steuerabtastperiode ein Steuersignal für jeden der vielen Stellparameter unter Verwendung der Steuermatrix und der Zielwerte für die Teilmenge der mehreren Steuer- und Hilfsgrößen erzeugt, bei dem die Steuersignale dazu bestimmt sind, die Teilmenge der mehreren der Steuer- und Hilfsparameter auf die für die Teilmenge von Steuer- und Hilfsparametern optimalen Zielwerte zu fahren;
  • bei dem der Funktionsblock einen Speicher zum Speichern eines Satzes von Steuerparametersollwerten und eines Satzes von Hilfs- und Stellparametergrenzen umfasst, und bei dem das Optimierungsprogramm so konfiguriert ist, dass es den Satz von optimalen Zielwerten für die Stellparameter, die die Steuerparameter ergeben, sich auf den Steuerparametersollwerten, die Hilfs- und Stellparameter sich innerhalb der Hilfs- und Stellparametergrenzen befinden und die Zielfunktion minimiert oder maximiert wird;
  • bei dem der Speicher auch einen Satz von Steuerparametersollwertgrenzen gespeichert hat und das Optimierungsprogramm dazu ausgelegt ist, den Satz von optimalen Zielwerten für die Stellparameter herzustellen, die die Zielfunktion maximieren oder minimieren, während jeder der Steuerparameter innerhalb der Steuerparametersollwertgrenzen und jeder der Hilfsparameter und Stellparameter innerhalb der Hilfs- und Stellparametergrenzen gehalten wird, wenn keine Lösung besteht, die die Steuerparameter auf den Steuerparametersollwerten und die Hilfs- und Stellparameter innerhalb der Hilfs- und Stellparametergrenzen hält;
  • bei dem der Speicher auch einen Satz von Prioritätsangaben für die Steuerparameter gespeichert hat und das Optimierungsprogramm dazu ausgelegt ist, den Satz von Zielstellparametern herzustellen, die die Zielfunktion maximieren oder minimieren, während jeder der Steuerparameter innerhalb der Steuerparametersollwertgrenzen gehalten wird, während eine oder mehrere der Steuerparameter die Steuerparametersollwertgrenzen basierend auf den Prioritätsangaben für die Steuerparameter verletzen darf/dürfen, wenn keine Lösung besteht, die jeden der Steuerparameter innerhalb der Steuerparametersollwertgrenzen und jede der Hilfsparameter und Stellparameter innerhalb der Hilfs- und Stellparametergrenzen hält;
  • bei dem der Speicher auch einen Satz von Prioritätsangaben für die Hilfsparameter gespeichert hat und das Optimierungsprogramm dazu ausgelegt ist, den Satz von Zielstellparametern herzustellen, die die Zielfunktion maximieren oder minimieren, während mindestens einer der Hilfsparameter die Hilfsparametergrenzen basierend auf den Prioritätsangaben für die Hilfsparameter verletzten darf, und die Steuerparameter die Steuerparametersollwertgrenzen basierend auf den Prioritätsangaben für die Steuerparameter verletzen dürfen, wenn keine Lösung besteht, die jeden der Steuerparameter innerhalb der Steuerparametersollwertgrenzen und jeden der Hilfsparameter und Stellparameter innerhalb der Hilfs- und Stellparametergrenzen hält.

A process control element adapted to be used as part of a process control program implemented on a processor to control multiple control and auxiliary parameters of a process using multiple manipulated parameters, the process control element comprising:

• a computer-readable medium;
• a function block stored on the computer readable medium and adapted to run on the processor to effectuate multiple input/multiple output control of the process during each control sampling period, the functional block comprising:
  • an objective function defining an optimization criterion based on the plurality of control and auxiliary parameters;
  • an optimization program that uses the objective function to produce a set of optimal target values for the control and auxiliary parameters during each control sample period;
  • a control matrix relating a predetermined subset of the plurality of control and auxiliary parameters to the plurality of adjustment parameters; and
  • a multiple-input/multiple-output control program that generates a control signal for each of the plurality of control parameters during each control sample period using the control matrix and the target values for the subset of the plurality of control and auxiliary variables, wherein the control signals are designed to control the subset of the plurality of the drive control and auxiliary parameters to the optimal target values for the subset of control and auxiliary parameters;
  • wherein the functional block includes a memory for storing a set of control parameter setpoints and a set of auxiliary and manipulated parameter limits, and wherein the optimization program is configured to calculate the set of optimal target values for the manipulated parameters that result in the control parameters on the control parameter setpoints, the auxiliary and manipulated parameters are within the auxiliary and manipulated parameter limits and the objective function is minimized or maximized;
  • wherein the memory also has stored a set of control parameter setpoint limits and the optimizer is adapted to produce the set of optimal target values for the manipulated parameters that maximize or minimize the objective function while each of the control parameters is within the control parameter setpoint limits and each of the auxiliary parameters and manipulated parameters is within the maintaining auxiliary and manipulated parameter limits when no solution exists to keep the control parameters at the control parameter setpoints and the auxiliary and manipulated parameters within the auxiliary and manipulated parameter limits;
  • wherein the memory also has stored a set of priority indications for the control parameters and the optimizer is adapted to produce the set of target setting parameters that maximize or minimize the target function while each of the control parameters is within control parameter setpoint limits is maintained while one or more of the control parameters is permitted to violate the control parameter setpoint limits based on the priority designations for the control parameters, if there is no solution that keeps each of the control parameters within the control parameter setpoint limits and each of the auxiliary and manipulated parameters within the auxiliary and manipulated parameter limits;
  • wherein the memory also has stored a set of priority specifications for the ancillary parameters and the optimizer is designed to produce the set of objective setting parameters that maximize or minimize the objective function while at least one of the auxiliary parameters is allowed to violate the auxiliary parameter limits based on the priority specifications for the auxiliary parameters , and the control parameters are allowed to violate the control parameter set point limits based on the priority designations for the control parameters if there is no solution keeping each of the control parameters within the control parameter set point limits and each of the auxiliary and manipulated parameters within the auxiliary and manipulated parameter limits.

32nd embodiment

• eine Schrittantwortmatrix zu bestimmen, die eine Antwort jeder der Steuer- und Hilfsgrößen auf Veränderungen bei jeder der Stellgrößen definiert;
• eine Teilmenge der Steuer- und Hilfsgrößen auszuwählen, wobei die Teilmenge dieselbe wie oder eine geringere Anzahl von Steuer- und Hilfsgrößen aufweist als die Stellgrößen, bei dem das Auswählen der Teilmenge das Auswählen einer der am stärksten auf eine der Stellgrößen ansprechenden Steuer- oder Hilfsgrö-ßen umfasst;
• eine quadratische Steuermatrix aus dem Antworten innerhalb der Antwortmatrix für die ausgewählte Teilmenge der Steuer- und Hilfsgrößen und der Stellgrößen herzustellen; und
• während jeder Abtastung des Prozesses
• einen Messwert von jeder der ausgewählten Teilmenge der Steuer- und Hilfsgrößen zu erhalten;
• einen optimalen Betriebszielwert für jede der ausgewählten Teilmenge der Steuer- und Hilfsgrößen zu berechnen;
• ein Mehrfacheingabe-/Mehrfachausgabe-Steuerprogramm ablaufen zu lassen, das die Zielwerte für jede der ausgewählten Teilmenge der Steuer- und Hilfsgrö-ßen, die Messwerte der ausgewählten Teilmenge der Steuer- und Hilfsgrößen und die Steuermatrix verwendet, um einen Satz von Stellparametersignalen zu erzeugen; und
• die Stellparametersignale zur Steuerung des Prozesses zu verwenden.

Method for performing the control of a process with a first number of control and auxiliary variables controlled by a second number of manipulated variables, the method comprising:

• determine a step response matrix defining a response of each of the control and auxiliary variables to changes in each of the manipulated variables;
• to select a subset of the control and auxiliary variables, the subset having the same or a smaller number of control and auxiliary variables than the manipulated variables, in which selecting the subset involves selecting one of the control or auxiliary variables most responsive to one of the manipulated variables includes;
• construct a square control matrix from the responses within the response matrix for the selected subset of the control and auxiliary variables and the manipulated variables; and
• during each scan of the process
• obtain a measurement of each of the selected subset of the control and auxiliary quantities;
• calculate an optimal operating target value for each of the selected subset of the control and auxiliary quantities;
• run a multiple input/multiple output control program that uses the target values for each of the selected subset of the control and auxiliary quantities, the measured values of the selected subset of the control and auxiliary quantities, and the control matrix to generate a set of manipulated variable signals; and
• to use the control parameter signals to control the process.

33rd embodiment

method according to embodiment 32,
wherein selecting one of the control or auxiliary variables most responsive to one of the manipulated variables comprises selecting one of the control or auxiliary variables based at least on a cross correlation analysis.

34th embodiment

Method according to embodiment 32 or 33,
wherein selecting one of the control or auxiliary variables most responsive to one of the manipulated variables includes selecting one of the control or auxiliary variables based at least on a heuristic method.

35th embodiment

Method according to one of the embodiments 32 to 34,
in which selecting one of the most responsive to one of the manipulated variables control or auxiliary includes selecting one of the control or auxiliary variables based at least on priorities associated with the control and auxiliary variables.

Claims (27)

Process control configuration system for use in a process control system (10) for configuring a control block (38) with an integrated optimizer (48) and a multiple input/multiple output control routine, the process control system comprising: a computer-readable medium: a configuration routine stored on the computer-readable medium and adapted to run on a processor, the configuration routine including: a storage routine that stores information representing a plurality of control and auxiliary variables (CV, AV) with a set of target values generated by the optimizer (48) and provided to a multiple input/multiple output controller during each cycle of operation of the process control system (10). , and relates to a plurality of manipulated variables (MV) used by the optimizer (48; 54) and/or the multiple input/multiple output control routine, the information relating to the plurality of control and auxiliary variables and the plurality of manipulated variables including response information for each of at least some of the control and auxiliary variables indicating response information of respective responses for each of the at least some of the control and auxiliary variables to respective manipulated variables; and a display routine designed to provide a user with a display (14) in terms of one or more of the control, auxiliary, target and manipulated variables (CV, AV, MV), the display comprising a subset of the response information, wherein the subset of the response information includes response information indicative of responses from each of the at least some of the target, control, and auxiliary variables to at least one of the manipulated variables. Process Control Configuration System claim 1 wherein the configuration routine further includes a first routine to allow the user to select the one of the plurality of manipulated variables (MV). Process Control Configuration System claim 2 wherein the configuration routine further includes a second routine to allow the user to associate one of the manipulated variables (MV) with one of the control and auxiliary variables (CV, AV). Process Control Configuration System claim 3 wherein the configuration routine further includes a third routine to allow the user to unassign one of the control and auxiliary variables (CV, AV) associated with one of the manipulated variables (MV). Process control configuration system according to one of Claims 1 until 4 wherein the display routine is adapted to display indications of the plurality of manipulated variables (MV) and, for each manipulated variable, an indication of the associated plurality of control and auxiliary variables (CV, AV), if any, to display. Process control configuration system according to one of Claims 1 until 5 wherein the display routine is adapted to display an indication of a current configuration condition number associated with a configuration corresponding to the currently associated manipulated variables (MV) and control and auxiliary variables (CV, AV). Process control configuration system according to one of Claims 1 until 6 wherein the display routine is configured to display an indication of an automatic configuration condition number associated with an automatically generated configuration. Process control configuration system according to one of the preceding claims, wherein the display routine is adapted to display an indication of a process matrix configuration condition number associated with a configuration corresponding to a process matrix that all of the plurality of control and auxiliary variables (CV, AV) along a first axis of the process matrix and all of the manipulated variables (MV) along a second axis of the process matrix. Process control configuration system according to one of the preceding claims, wherein the display routine is designed to display indications of available variables of the plurality of control and auxiliary variables (CV, AV), the available variables being associated with manipulated variables (MV). A process control configuration system as claimed in any preceding claim, wherein the display routine is adapted to display response information indications for each of the available variables, the response information indicating responses from each of the available variables to the one of the manipulated variables (MV). The process control configuration system of any preceding claim, wherein the response information for each of the available quantities includes at least an indication of a gain (220), an indication of a dead time (224), an indication of a priority, and an indication of a time constant. Process Control Configuration System claim 11 , where the gain indication (220) includes a number. Process Control Configuration System claim 11 or 12 , where the dead time specification (224) includes a number. Process control configuration system according to one of the preceding claims, wherein the response information for each of the at least some of the control and auxiliary variables (CV, AV) at least an indication of a gain (220), an indication of a dead time (224), an indication of a priority and a Specification of a time constant contains. Process control configuration system according to one of the preceding claims, wherein the response information for each of the at least some of the control and auxiliary variables (CV, AV) includes information associated with a step response. Process control configuration system according to one of the preceding claims, wherein the response information for each of the at least some of the control and auxiliary variables (CV, AV) includes data associated with a pulse response. A process control configuration system as claimed in any preceding claim, wherein the response information for each of the at least some of the control and auxiliary quantities (CV, AV) includes information associated with an increase response. A method of using a process control system to control a process (50) with multiple manipulated variables (MV) and many control and auxiliary variables (CV, AV) that can be affected by changes in the manipulated variables, in which the multiple of the manipulated variables differ in number from the distinguish between many control and auxiliary variables, whereby the procedure includes: select a subset of the plurality of control and auxiliary variables to be used in performing process control, wherein selecting the subset comprises selecting one of the control or auxiliary variables that is most responsive to one of the manipulated variables; construct a control matrix using the selected subset of the plurality of the control and auxiliary variables and the plurality of manipulated variables; generate a controller from the control matrix with the selected subset of the plurality of the control and auxiliary variables as inputs and the plurality of manipulated variables as outputs; using an optimizer (48; 54) to carry out process optimization by selecting a process operating point in order to minimize or maximize a target function depending on the multiple manipulated variables and the multiple control and auxiliary variables, the process operating point being defined by a set of target values for the selected subset of the many control and auxiliary variables is defined; providing the set of target values to a multiple input/multiple output controller during each cycle of operation of the process control system (10); using the multiple-input/multiple-output control to perform a multiple-input/multiple-output control method using a controller generated from the control matrix to select a set of manipulated variable values from the target values for the selected subset of the many control and auxiliary variables and measured values of the selected ones to form a subset of the many control and auxiliary variables; and to use the formed set of manipulated variable values to control the process. procedure after Claim 18 , wherein selecting one of the control or auxiliary variables (CV, AV) as the most responsive to one of the manipulated variables (MV) comprises selecting one of the control or auxiliary variables based at least on a cross-correlation analysis. procedure after Claim 18 or 19 , wherein selecting one of the control or auxiliary variables (CV, AV) as the most responsive to one of the manipulated variables (MV) comprises selecting one of the control or auxiliary variables based on at least one heuristic method. Procedure according to one of claims 18 until 20 wherein selecting one of the control or auxiliary variables as most responsive to one of the manipulated variables (MV) comprises selecting one of the control or auxiliary variables (CV, AV) based at least on priorities associated with the control and auxiliary variables . A method of using a process control system to control a process (50) having multiple manipulated variables (MV) and many control and auxiliary variables (CV, AV) that can be affected by changes in the manipulated variables, where the plurality of manipulated variables are numerically from the many control and auxiliary variables, whereby the procedure includes: select a subset of the plurality of control and auxiliary variables to be used in performing the process control in which a number of control and auxiliary variables in the subset is less than a number of manipulated variables in the plurality of manipulated variables; construct a control matrix using the selected subset of the plurality of the control and auxiliary variables and the plurality of manipulated variables; generate a controller from the control matrix with the selected subset of the plurality of the control and auxiliary variables as inputs and the plurality of manipulated variables as outputs; using an optimizer (48) to carry out process optimization by selecting a process operating point in order to minimize or maximize a target function depending on the multiple manipulated variables and the many control and auxiliary variables, the process operating point being determined by a set of target values for the selected subset of the many control and auxiliary variables is defined; providing the set of target values to a multiple input/multiple output controller during each cycle of operation of the process control system (10); using the multiple-input/multiple-output control to perform a multiple-input/multiple-output control method using a controller generated from the control matrix to select a set of manipulated variable values from the target values for the selected subset of the many control and auxiliary variables and measured values of the selected ones form a subset of the many control and auxiliary variables; and use the trained set of manipulated variable values to control the process. A process control element configured to be used as part of a process control program implemented on a processor to control multiple control and auxiliary parameters (CV, AV) of a process (50) using multiple manipulated parameters (MV), the process control element comprising : a computer-readable medium; a function block stored on the computer readable medium and adapted to run on the processor to effectuate multiple input/multiple output control of the process during each control sampling period, the functional block comprising: an objective function defining an optimization criterion based on the plurality of control and auxiliary parameters, wherein the objective function defines an optimization criterion based on a first number of control and auxiliary parameters; an optimizer that uses the objective function to produce a set of optimal target values for the control and auxiliary parameters during each control sample period, wherein the optimizer includes a linear or quadratic programming routine, and wherein the optimizer applies the set of optimal target values to a multiple input/multiple output - provides control program during each operation cycle; a control matrix relating a predetermined subset of the plurality of control and auxiliary parameters to the plurality of adjustment parameters, in which a number of control and auxiliary parameters in the predetermined subset is equal to the first number, in which a number of adjustment parameters in the plurality setting parameters is equal to the first number; and the multiple-input, multiple-output control program that generates, during each control sample period, a control signal for each of the plurality of manipulated parameters using the control matrix and the target values for the subset of the plurality of controls and auxiliary quantities, wherein the control signals are designed to control the subset of the plurality of the drive control and auxiliary parameters to the optimal target values for the subset of control and auxiliary parameters, wherein the multiple-input/multiple-output handler comprises a model prediction handler. A method of performing control of a process (50) having a first number of control and auxiliary variables (CV, AV) controlled by a second number of manipulated variables (MV), the method comprising: determining a step response matrix that defines a response of each of the control and auxiliary variables to changes in each of the manipulated variables; select a subset of the control and auxiliary variables, the subset having the same or fewer number of control and auxiliary variables than the manipulated variables, wherein selecting the subset comprises selecting one of the control or auxiliary variables most responsive to one of the manipulated variables ; construct a square control matrix from the responses within the response matrix for the selected subset of the control and auxiliary variables and the manipulated variables; and during each scan of the process, obtain a measurement of each of the selected subset of the control and auxiliary variables; calculate, by means of an optimizer (48; 54), an optimal operating target value for each of the selected subset of the control and auxiliary variables; providing the operational target value for each of the selected subset of the control and auxiliary quantities from the optimizer to a multiple input/multiple output controller during each cycle of operation of the process control system; to run, by means of the multiple input/multiple output controller, a multiple input/multiple output control program that uses the target values for each of the selected subset of the control and auxiliary variables, the measured values of the selected subset of the control and auxiliary variables, and the control matrix to produce a generate set of setting parameter signals; and use the manipulated variable signals to control the process. procedure after Claim 24 , wherein selecting one of the control or auxiliary variables (CV, AV) most responsive to one of the manipulated variables (MV) comprises selecting one of the control or auxiliary variables based at least on a cross-correlation analysis. procedure after Claim 24 or 25 , wherein selecting one of the control or auxiliary variables (CV, AV) most responsive to one of the manipulated variables (MV) comprises selecting one of the control or auxiliary variables based at least on a heuristic method. Procedure according to one of claims 24 until 26 wherein selecting one of the control or auxiliary variables (CV, AV) most responsive to one of the manipulated variables (MV) comprises selecting one of the control or auxiliary variables based at least on priorities associated with the control and auxiliary variables.

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