CN112068431A - Control method, system and device for double time scales and storage medium - Google Patents
Control method, system and device for double time scales and storage medium Download PDFInfo
- Publication number
- CN112068431A CN112068431A CN202010915287.6A CN202010915287A CN112068431A CN 112068431 A CN112068431 A CN 112068431A CN 202010915287 A CN202010915287 A CN 202010915287A CN 112068431 A CN112068431 A CN 112068431A
- Authority
- CN
- China
- Prior art keywords
- control
- variable
- steady
- value
- error correction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a control method, a system, a device and a storage medium of double time scales, wherein the method comprises the following steps: acquiring a control variable, and constructing a dual-time scale model according to the control variable; determining a class integral variable according to the control variable, and determining a steady-state target value of the controlled variable according to the double-time scale model; tracking the steady-state target value through dynamic control to obtain a set value of bottom control, performing real-time control according to the set value, and acquiring an actual value of the real-time control; carrying out error compensation on the predicted value of the state control according to the actual value, and carrying out control and tuning according to the predicted value after the error compensation is finished; the method reduces the error caused by model truncation, can keep the prediction control calculation amount based on the truncation model to be closer to the steady-state target value of the real model, and provides a new thought for a steady-state target layer; the accumulated error is reduced through dynamic control, so that the steady-state control is realized, and the method can be widely applied to the technical field of automatic control.
Description
Technical Field
The invention belongs to the technical field of automatic control, and particularly relates to a control method, a control system, a control device and a storage medium with double time scales.
Background
In an actual production process, the physical characteristics of the variables of the multi-time scale system are greatly different, and the response speed of each object in the multi-time scale system is different, so that the multi-time scale problem is generated. The multi-timescale problem is manifested in many areas, such as combustion engine systems.
The Model Predictive Control (MPC) algorithm has better processing capability for multivariable Control problems in industrial processes, DMC (dynamic Matrix Control) is a typical representative in the MPC algorithm development process, a step response Model is adopted in a prediction time domain, and the method has wide engineering application, wherein the mature software DMCplus technology in the united states is developed based on DMC. The domestic scholars propose that a double-layer structure model predictive control algorithm is divided into a steady-state control layer and a dynamic optimization layer, so that the development of the MPC is more diversified and the MPC has more and more powerful functions.
In the existing analysis of the predictive control of the double-layer structure model, the controlled object is generally in the same time scale, and the analysis of the system is not carried out on the problem of double time scales existing in a complex system. When a model is built in a double-layer MPC, the response of an object is required to reach a steady state, and particularly a steady-state gain matrix of a steady-state optimization layer needs a steady-state model of a process, so that a modeling time domain needs to meet the requirements. However, for the problem of double time scales, the difference of time for the fast and slow models to reach a steady state is large, the centralized control of the fast and slow models needs unified modeling, if only the stability of the slow model is considered during modeling, the adoption of a large modeling time domain based on the slow model leads to the lengthening of the calculation time, and the fast model reaches a steady state value early, so that a large amount of redundant calculation is generated to increase the burden; if only the modeling time domain with the time for the fast model to reach the steady state is considered, the slow model does not reach the steady state, model mismatch causes inaccurate control and can not accurately track an external target, and the slow model converges to the steady state value with a slow speed and does not meet the steady state model required by the steady state optimization layer.
Disclosure of Invention
In view of the above, to at least partially solve one of the above technical problems, embodiments of the present invention provide a dual-timescale control method, which makes a steady-state target calculation closer to a real steady-state target value of a model in case of model mismatch, and a system, an apparatus, and a storage medium that can correspondingly implement the dual-timescale control method.
In a first aspect, an embodiment of the present invention provides a dual-timescale control method, which includes the following steps:
acquiring a control variable, and constructing a dual-time scale model according to the control variable;
determining a class integral variable according to the control variable, and determining a steady-state target value of the controlled variable according to the double-time scale model;
tracking the steady-state target value through dynamic control to obtain a set value of bottom control, performing real-time control according to the set value, and acquiring an actual value of the real-time control;
and carrying out error compensation on the predicted value of the dynamic control according to the actual value, and carrying out control and tuning according to the predicted value after completing the error compensation.
In some embodiments of the present invention, performing error compensation on the predicted value of the dynamic control according to the actual value, and performing control and tuning according to the predicted value after completing the error compensation, which specifically includes: and introducing a prediction error to generate a prediction error compensation factor, and performing error compensation on the dynamic control process according to the prediction error compensation factor.
In some embodiments of the present invention, the step of determining a class integral variable according to the control variable, and determining a steady-state target value of the controlled variable according to the dual-time scale model specifically includes:
truncating the class integral variable to generate a step response of the double-time scale model;
generating a free predicted value of the class integral variable and a predicted value sequence under the action of the control variable based on the characteristic of the step response;
introducing auxiliary variables into the prediction sequence, and constructing a quadratic programming problem by combining constraint conditions of the class integral variables;
determining a manipulated variable through a quadratic programming problem, determining a controlled variable according to the manipulated variable, and determining a steady-state target value according to the manipulated variable and the controlled variable.
In some embodiments of the present invention, the step of introducing a prediction error compensation factor and performing error compensation on the dynamically controlled process according to the prediction error compensation factor comprises the steps of:
generating an error correction coefficient vector according to the prediction error compensation factor;
obtaining an error correction matrix according to the error correction coefficient vector;
and according to the error correction matrix, carrying out error compensation on the quasi-integral controlled variable.
In some embodiments of the invention, the error correction matrix further comprises a non-integral-like variable error correction coefficient vector.
In some embodiments of the invention, the dual timescales comprise the following: slow model variables and fast model variables.
In a second aspect, the technical solution of the present invention further provides a dual time scale control system, which includes a signal input unit, a signal output unit, and a prediction control unit; wherein:
a signal input unit for acquiring a control variable;
the signal output unit is used for determining a set value of the bottom layer control quantity;
the prediction control unit comprises a steady-state optimization layer and a dynamic control layer and is used for tracking the dynamic control layer according to a steady-state target value generated by the double-time scale model;
the steady state optimization layer is used for determining a similar integral variable according to the control variable and obtaining a steady state target value of the control variable and the controlled variable through steady state optimization;
the dynamic control layer is used for tracking the steady-state target value through dynamic control to obtain a set value of bottom control, performing real-time control according to the set value and acquiring an actual value of the real-time control;
the dynamic control layer also comprises an error correction subunit, which is used for generating a corresponding error correction coefficient vector according to the error compensation factor, generating an error correction matrix according to the error correction coefficient vector, and performing error correction on the predicted value of the dynamic control layer according to the error correction matrix.
In a third aspect, the present invention further provides a dual-timescale control apparatus, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the dual timescale control method of the first aspect.
In a fourth aspect, the present invention also provides a storage medium in which a processor-executable program is stored, the processor-executable program being configured to implement the method as in the first aspect when executed by a processor.
Advantages and benefits of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
the invention provides a control method of double time scales, in the process of obtaining a steady-state target value through a double time scale model, the step response characteristic of a class integral variable is introduced into the calculation of a steady-state optimization layer, the error caused by model truncation is reduced in the process, the predicted control calculated quantity based on a truncation model can be closer to the steady-state target value of a real model, and a new thought is provided for the steady-state target layer; in the dynamic control process, an error compensation factor corresponding to the dynamic control process is provided to reduce the accumulated error caused by the quasi-integral variable, so that the aim of steady-state control is fulfilled.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart illustrating steps of a dual-timescale control method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of generating a steady-state target value in a dual-timescale control method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a dual-timescale control apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
The general idea of the invention is as follows: aiming at the problem of double time scales in the prior art, the invention discloses a double-layer structure prediction control method of a double time scale system under model truncation, which mainly solves the problems that a steady-state optimization model does not reach a steady state and errors are accumulated in dynamic control. The method can not only keep the prediction control calculated amount based on the truncation model, but also obtain the steady-state target value closer to the real model, and provides a new thought for the steady-state target layer.
In a first aspect, an embodiment of the present invention provides a dual-time scale control system, which includes a signal input unit, a signal output unit, and a prediction control unit; wherein: and the signal input unit is used for acquiring the control variable. And the signal output unit is used for determining the set value of the bottom layer control quantity.
The prediction control unit in the system comprises a steady state optimization layer and a dynamic control layer, and is used for tracking the dynamic control layer according to a steady state target value generated by a double time scale model. Specifically, the steady-state optimization layer is used for determining a similar integral variable according to the control variable and obtaining a steady-state target value of the control variable and the controlled variable through steady-state optimization; and the dynamic control layer is used for obtaining a set value for bottom layer control through dynamic control according to the steady-state target value. The dynamic control layer also comprises an error corrector subunit, which is used for generating a corresponding error correction coefficient vector according to the error compensation factor, generating an error correction matrix according to the error correction coefficient vector, and predicting error correction for the controlled variable according to the error correction matrix.
In a second aspect, as shown in fig. 1, an embodiment of the present invention further provides a control method based on the system embodiment in the first aspect: a control method of double time scales mainly comprises steps S01-S03:
s01, obtaining a control variable, and constructing a double-time scale model according to the control variable;
s02, determining a class integral variable according to the control variable, and determining a steady-state target value of the controlled object according to the double-time scale model; wherein, as shown in fig. 2, the method comprises steps S021-S024:
s021, cutting off the class integral variable to generate a step response of the double-time scale model; specifically, in the embodiment, a "point" model strategy of an integral variable is adopted, and as time changes, a step response of the integral variable is a straight line with a constant slope; for slow model variables, which are truncated in advance before reaching the steady state value, have a step response curve similar to the integral variable, they can be called as quasi-integral variables. But the quasi-integral variable has an important difference from the integral variable, and the steady-state rate gain of the integral variable is a constant value after a certain point; the steady-state rate gain of the quasi-integral variable still retains the step response characteristic of the conventional variable, increases like an exponential, and eventually reaches steady state as long as the time is long enough. This means that the integral variable rate balance constraint determined by the steady state rate gain constancy feature is not applicable to the integral-like variable. In the present implementation systems and methods, the variables of the dual-time scale model, i.e., the dual-time scale, may include slow model variables and fast model variables.
In this embodiment, a "point" model strategy based on an integral variable is implemented by first truncating a quasi-integral variable at time N and using a step response value of the point as a model gain at a steady-state time point; secondly, the step response characteristic of the integral-like variable is utilized
The future output of the method has exponential characteristics, and a free prediction value y (k + N +1| k-1) after the N moment can be deduced; meanwhile, the predicted value of the future moment under the action of m control variables can be obtained, and the predicted value is introduced into the calculation of the steady-state objective function to express the future controlled variable value, namely the future controlled variable value
And finally, solving a QP problem of a target tracking mode conforming to the similar integral variable characteristics, and taking the control variable at the mth moment and the controlled variable corresponding to the control variable as a steady-state target value.
S022, based on the characteristics of step response, generating a free predicted value of a class integral variable and a predicted value sequence under the action of a control variable; in particular, the control variable uiStarting from time k, there are m incremental changes { Δ u (k) } … Δ u (k + m-1) }, let σ be exp (-T)0T), the predicted value expression form of k + m + N under the control increment of m steps is as follows:
wherein the content of the first and second substances,
namely, the matrix expression of the calculation formula (1) is as follows:
in the calculation formula (4), there are:
s023, introducing auxiliary variables into the prediction sequence, and constructing a quadratic programming problem by combining constraint conditions of the class integral variables; specifically, an auxiliary variable is introduced to construct a Quadratic Programming (QP) problem, and the QP problem is expressed in a matrix. In the steady state optimization solution, the decision variables of the QP problem are set to { Δ u (k) … Δ u (k + m-1) }, and Δ ub(k) The variables involved are {0, Δ u (k) … Δ u (k + m-2) }, so Δ u cannot be directly represented by the decision variable table in the formed QP problemb(k) In that respect Therefore, introduce an auxiliary variable Δ us(k)：
Using the calculations of equations (1), (2) and (3):
Δub(k)＝σ(Δua(k)-Δus(k)) (8)
substituting calculation formula (8) into calculation formula (1) can obtain:
the resulting calculated formula (9) is expressed in matrix form as:
obtaining future predicted value of controlled variable under the action of a series of control variable increments, and comparing the future predicted value with an external target yoptAnd uoptThe least squares method is used, so the problem of target tracking of the integral-like steady state optimization can be expressed as follows:
s.t.
uTmin≤um(k)≤uTmax
wherein:
in the calculation formulas (12) and (13), Q, V and R are weight coefficient matrixes and are positive definite or semi-positive definite matrixes; to output constraint relaxation coefficients; u. ofTminAnd uTmaxConstraint lower and upper bounds for steady state inputs; Δ uTminAnd Δ uTminConstraint lower and upper bounds for steady state input deltas; u. ofTmin、uTmax、ΔuTmin、ΔuTminAnd um(k) All are nm multiplied by 1 dimension column vector matrixes, wherein n is the number of control variables; y isTminAnd yTmaxAnd the lower bound and the upper bound of the constraint of steady-state output are pm multiplied by 1 dimension column vector matrixes, and p is the number of the control-class integral controlled variables. B is0Is a lower triangular matrix of dimension m x m, n blocks B0Form a matrix B ═ block-diag (B)0，…B0)。
S024, determining a manipulated variable through a quadratic programming problem, determining a controlled variable according to the manipulated variable, and determining a steady-state target value according to the manipulated variable and the controlled variable. Wherein QP problemWith the aid of an auxiliary variable Δ us(k) Expressed decision variable Δ ub(k) I.e., as manipulated variables. Solving a quadratic programming problem to obtain steady-state target values of the control variables and the controlled variables; and finally solving the target tracking mode QP problem formed in the step S023, and transmitting the mth control variable and the controlled variable corresponding to the mth control variable as a steady-state target value to the dynamic control layer. Specifically, steady-state target values y (k + N + m | k + m-1) and u (k + m-1) obtained by the steady-state optimization layer are transmitted to the dynamic control layer and are used as steady-state target values y in the k-time optimization performance index after filtering*(∞ | k + m-1) and u*(∞ | k + m-1), forming a QP problem that the dynamic control layer has constraints.
S03, tracking the steady-state target value through dynamic control to obtain a set value of bottom control, performing real-time control according to the set value, and acquiring an actual value of the real-time control; and carrying out error compensation on the predicted value of the dynamic control according to the actual value, and carrying out control and tuning according to the predicted value after completing the error compensation. The dynamic control layer increases an error correction factor corresponding to the quasi-integral step response to reduce the accumulation of last bit errors caused by the early truncation model.
In some implementations of the method, step S03 may specifically be: and introducing a prediction error to generate a prediction error compensation factor, and performing error compensation on the dynamic control process according to the prediction error compensation factor. Specifically, according to the step response characteristic of the quasi integral variable, an error compensation factor is introduced into an error correction matrix of the dynamic control layer; and the unmeasured disturbance variable in the class integration process also has step response characteristics, and the serious prediction error accumulation can be caused by the conventional one-step prediction error correction, so that for the class integration variable process, a displacement matrix sigma is used as a prediction error compensation factor, an error correction coefficient vector corresponding to the step response model is formed, and an error correction matrix is constructed.
In this embodiment, step S03 is further subdivided into steps S031-S033:
s031, according to predicting the error compensation factor and producing the error correction coefficient vector;
s032, obtaining an error correction matrix according to the error correction coefficient vector;
specifically, for the integral-like variable process, using σ of the displacement matrix as a prediction error compensation factor, an error correction coefficient vector corresponding to the step response model is formed:
hrr＝[1，1+σ，1+2a，…，1+(N-1)σ]T (17)
and S033, performing error compensation on the class integral controlled variable according to the error correction matrix.
Furthermore, in an embodiment, the error correction matrix further comprises a non-integral-like variable error correction coefficient vector. Specifically, an error correction matrix is obtained according to the non-class integral variable error correction coefficient vector and the error correction coefficient vector. For non-class integral variable error correction coefficient vector is hiiAnd the finally obtained error correction matrix is as follows:
and performing error compensation according to the error correction matrix. Step S032 is integrated, and an error compensation factor is introduced into the error displacement matrix h; in addition, in order to compensate errors caused by the advanced truncation of the last bit, the predicted displacement matrix M is changed from an original translation formula to an exponential recursion formula; and building a dynamic control layer of the double-time scale system.
And S04, performing error compensation on the predicted value of the dynamic control according to the actual value, and performing control and tuning according to the predicted value after the error compensation is completed.
The system and the method of the embodiment of the invention can be applied to a combustion engine system, and the power response speed of each device in the system is different, so that the system is a multi-time-scale coupling system. For example: as a primary energy source, namely a natural gas conversion system, the efficiency of the CCHP system is greatly influenced, so that the CCHP system needs to be operated near an optimal working point. The natural gas is efficiently combusted by controlling the air quantity; and the natural gas amount is controlled to adjust the overall energy of the system, so that the requirement of an external network on cold/heat power is met. For another example: the electric power of the gas turbine needs 60s when reaching a steady state, the electric power of the combined supply system is 500s, and the cold/hot power of the combined supply system is more than 4000s, so that the problem of multiple time scales can be described. In the two scenarios illustrated, a dual-time scale system formed according to how fast and slow the response of an object is typical of a multi-time scale system.
In a third aspect, as shown in fig. 3, the embodiment of the present invention further provides an apparatus for automatic matching based on subject words and sentence themes, which includes at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor is caused to implement a dual timescale control method as in the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a storage medium storing a program, where the program is executed by a processor, where the method in the first aspect is performed.
From the above specific implementation process, it can be concluded that the technical solution provided by the present invention has the following advantages or advantages compared to the prior art:
1. the method is based on a point model strategy of integral variables, and carries out comparative analysis on slow model variables and integral variables; for the 'false steady state' caused by truncation, the steady state optimization process utilizes the step response characteristic of the slow model variable to deduce a multi-step controlled variable predicted value after the truncation point. The invention introduces the step response characteristic of the similar integral variable into the design of the steady-state optimization layer, and better guides the lower layer to track the external target.
2. The dynamic control process is based on the improved displacement matrix, and error compensation factors are introduced to modify error correction coefficients so as to reduce accumulated errors caused by model truncation.
Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
Wherein the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A control method of double time scales is characterized by comprising the following steps:
obtaining a control variable, and constructing a dual-time scale model according to the control variable;
determining a class integral variable according to the control variable, and determining a steady-state target value of the controlled variable according to the double-time scale model;
tracking the steady-state target value through dynamic control to obtain a set value of bottom control, performing real-time control according to the set value, and acquiring an actual value of the real-time control;
and carrying out error compensation on the predicted value of the dynamic control according to the actual value, and carrying out control tuning according to the predicted value after error compensation is finished.
2. The method according to claim 1, wherein the step of performing error compensation on the dynamically controlled predicted value according to the actual value, and performing control tuning according to the predicted value after error compensation is specifically:
and introducing the prediction error to generate a prediction error compensation factor, and performing error compensation on the dynamic control process according to the prediction error compensation factor.
3. The dual-timescale control method of claim 1, wherein the step of determining a quasi-integral variable based on the control variable and determining a steady-state target value for a controlled variable based on the dual-timescale model comprises the steps of:
truncating the class integral variable to generate a step response of the double time scale model;
generating a free predicted value of the category integral variable and a predicted value sequence under the action of the control variable based on the characteristic of the step response;
introducing auxiliary variables into the prediction sequence, and constructing a quadratic programming problem by combining constraint conditions of the similar integral variables;
determining a manipulated variable through the quadratic programming problem, determining a controlled variable according to the manipulated variable, and determining the steady-state target value according to the manipulated variable and the controlled variable.
4. The dual time scale control method according to claim 2, wherein the step of introducing a prediction error compensation factor and performing error compensation on the dynamically controlled process according to the prediction error compensation factor comprises the steps of:
generating an error correction coefficient vector according to the prediction error compensation factor;
obtaining an error correction matrix according to the error correction coefficient vector;
and according to the error correction matrix, carrying out error compensation on the similar integral controlled variable.
5. The dual time scale control method of claim 4, wherein the error correction matrix further comprises a non-integral-like variable error correction coefficient vector.
6. A dual time scale control method according to any of claims 1-5, wherein the variables of the dual time scale model comprise slow model variables and fast model variables.
7. A control system with double time scales is characterized by comprising a signal input unit, a signal output unit and a prediction control unit; wherein:
the signal input unit is used for acquiring a control variable;
the signal output unit is used for determining a set value of the bottom layer control quantity;
the prediction control unit comprises a steady state optimization layer and a dynamic control layer and is used for tracking the dynamic control layer according to a steady state target value generated by the double time scale model;
the steady state optimization layer is used for determining a similar integral variable according to the control variable and obtaining a steady state target value of the control variable and the controlled variable through steady state optimization;
the dynamic control layer is used for tracking the steady-state target value through dynamic control to obtain a set value of bottom control, performing real-time control according to the set value, and acquiring an actual value of the real-time control;
the dynamic control layer also comprises an error correction subunit, which is used for generating a corresponding error correction coefficient vector according to the error compensation factor, generating an error correction matrix according to the error correction coefficient vector, and performing error correction on the predicted value of the dynamic control layer according to the error correction matrix.
8. A dual time scale control apparatus, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a dual timescale control method as claimed in any one of claims 1 to 6.
9. A storage medium having stored therein a program executable by a processor, characterized in that: the processor-executable program, when executed by a processor, is for implementing a dual timescale control method as recited in any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010915287.6A CN112068431B (en) | 2020-09-03 | 2020-09-03 | Control method, system and device for double time scales and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010915287.6A CN112068431B (en) | 2020-09-03 | 2020-09-03 | Control method, system and device for double time scales and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112068431A true CN112068431A (en) | 2020-12-11 |
| CN112068431B CN112068431B (en) | 2023-03-24 |
Family
ID=73665812
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010915287.6A Active CN112068431B (en) | 2020-09-03 | 2020-09-03 | Control method, system and device for double time scales and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112068431B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105759606A (en) * | 2015-12-24 | 2016-07-13 | 浙江中控软件技术有限公司 | Steady state target robust optimization method and device in view of model mismatch |
| CN108964544A (en) * | 2018-07-23 | 2018-12-07 | 中国矿业大学 | A kind of permanent magnet linear synchronous motor multiple time scale model System with Sliding Mode Controller and method |
| US20190067939A1 (en) * | 2016-05-18 | 2019-02-28 | China Electric Power Research Institute Company Limited | Multi-time-scale digital/analog hybrid simulation system and method for power distribution network and storage medium |
| CN109687510A (en) * | 2018-12-11 | 2019-04-26 | 东南大学 | A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method |
| CN110137942A (en) * | 2019-04-23 | 2019-08-16 | 河海大学 | Multiple Time Scales flexible load rolling scheduling method and system based on Model Predictive Control |
-
2020
- 2020-09-03 CN CN202010915287.6A patent/CN112068431B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105759606A (en) * | 2015-12-24 | 2016-07-13 | 浙江中控软件技术有限公司 | Steady state target robust optimization method and device in view of model mismatch |
| US20190067939A1 (en) * | 2016-05-18 | 2019-02-28 | China Electric Power Research Institute Company Limited | Multi-time-scale digital/analog hybrid simulation system and method for power distribution network and storage medium |
| CN108964544A (en) * | 2018-07-23 | 2018-12-07 | 中国矿业大学 | A kind of permanent magnet linear synchronous motor multiple time scale model System with Sliding Mode Controller and method |
| CN109687510A (en) * | 2018-12-11 | 2019-04-26 | 东南大学 | A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method |
| CN110137942A (en) * | 2019-04-23 | 2019-08-16 | 河海大学 | Multiple Time Scales flexible load rolling scheduling method and system based on Model Predictive Control |
Non-Patent Citations (2)
| Title |
|---|
| 庞强等: "双层结构预测控制中积分过程的稳态目标优化方法", 《信息与控制》 * |
| 苗萌萌等: "基于状态空间模型的双时间尺度预测控制算法", 《计算机与应用化学》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112068431B (en) | 2023-03-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113988481B (en) | Wind power prediction method based on dynamic matrix prediction control | |
| CN111723982A (en) | Medium-and-long-term power load combined prediction method based on gray-Markov chain | |
| CN111382581B (en) | One-time pruning compression method in machine translation | |
| CN107180279B (en) | QPSO-DMPC-based reaction regeneration system optimization control method | |
| CN112068431B (en) | Control method, system and device for double time scales and storage medium | |
| CN115390459A (en) | Model prediction control method and device | |
| CN112180738B (en) | Robust fuzzy prediction control method for nonlinear injection molding asynchronous switching process | |
| CN108009362B (en) | System modeling method based on stability constraint RBF-ARX model | |
| CN112000004B (en) | Sewage treatment concentration control method utilizing iterative quadratic heuristic programming | |
| Farbood et al. | Fuzzy model predictive MPPT control of interconnected wind turbines drivetrain | |
| CN108803342B (en) | Unit unit load quick response prediction control method | |
| CN111077771A (en) | Self-tuning fuzzy PID control method | |
| CN116522752A (en) | Compressed air energy storage system simulation method based on mechanism and data fusion | |
| CN113788007B (en) | Layered real-time energy management method and system | |
| CN115809725A (en) | Multi-factor short-term electric quantity prediction method and device | |
| CN113848726B (en) | Design method and device of system controller and electronic equipment | |
| CN112068423B (en) | Design method of finite power attraction repetitive controller | |
| CN114200840A (en) | Traditional Chinese medicine pharmacy process operation optimization method based on distributed model predictive control | |
| Choi et al. | Quantized-state-based decentralized neural network control of a class of uncertain interconnected nonlinear systems with input and interaction time delays | |
| CN113419432A (en) | Sewage treatment system accurate dosing method based on dynamic matrix control algorithm | |
| CN112925201B (en) | Model prediction control-based fuel control method for ship propulsion system | |
| CN110942150A (en) | Intelligent diagnosis method for forming failure of parts of selective laser melting equipment | |
| CN110825051A (en) | Multi-model control method of uncertainty system based on gap metric | |
| CN114383130B (en) | Method, device and equipment for controlling temperature of superheated steam of boiler and storage medium | |
| CN117057255B (en) | Pre-training model acquisition method for online synchronization of digital twin model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |