TW201546582A - Process control method - Google Patents

Abstract

A process control method is provided. In the process control method, at first, a plurality of process input data and a plurality of process output data of a processing machine are obtained. Thereafter, an operational reference mode of a Proportional Integral Derivative (PID) controller is selected from a plurality of reference modes. Then, a plurality of parameter calculating operations are performed to obtain a plurality of PID parameter sets and a plurality of error indicators corresponding to the PID parameter sets. Thereafter, a PID parameter set corresponding to a minimum error indicator of the error indicators is selected to control the PID controller for control the process of the processing machine. In the parameter calculating operation, at first, a time delay parameter is provided. Thereafter, the time delay parameter, the process input data, the process output data, and the corresponding error indicator are used to calculate the PID parameter set and a corresponding error indictor.

Description

Process control method

The invention relates to a process control method, in particular to a process control method using a Proportional Integral Derivative (PID) controller.

The PID controller is a closed loop controller, which has the advantages of simple structure, good reliability and convenient adjustment. Therefore, in the general industrial process, the PID controller is often used to control the process, and the design method of the PID controller is very important for the industrial process.

In the conventional PID controller design method, it is usually necessary to establish a model to obtain parameters of the PID controller to control the process. For example, first identify the low-order empirical model of the process, and then apply a specific tuning algorithm to design the PID controller. A good PID design can be obtained when the low-order model is sufficient to reasonably represent process dynamics. However, when applied to high-order process dynamics, the performance of the PID controller is degraded because of inevitable model errors. In addition, although the PID tuning algorithm contains tunable parameters, this tunable parameter can be traded appropriately between control performance and system stability resilience (to cope with model errors), so that better control performance is usually achieved. However, the optimum value of this tunable parameter often lacks a clear set of criteria and is usually determined by trial and error by additional process testing or closed loop simulation under known process dynamics.

For an operational control system, it is generally not feasible for conventional PID controller design methods to utilize closed loop testing to collect process data for model identification. Furthermore, for any PID design method based on the process model, whether the process model is sufficiently representative of the process dynamics is a key factor affecting the performance of the controller. In order to obtain a more accurate process model, it is necessary to use more complicated process testing and calculation process to identify the model, which makes the process process identification time-consuming and laborious, and greatly reduces the practicability of the design method.

Therefore, a new process control method is needed to improve the above problems.

One aspect of the present invention is to provide a process control method for designing a Proportional Integral Derivative (PID) controller directly from a process operation data (open loop test or closed loop test) without requiring a process to be identified. mode.

According to an embodiment of the present invention, in the process control method, first, a plurality of process input data and a plurality of process output data of the process device are obtained. Then, one of the plurality of reference modes selects a reference mode of operation of the PID controller. Then, a plurality of parameter calculation steps are performed to obtain a plurality of PID parameter groups of the PID controller and a plurality of error indicators corresponding to the PID parameter groups. In each parameter calculation step, a value of a delay parameter is first provided. Then, the PID parameter group and the error indicator of the PID controller are calculated by using the delay parameter, the process input data, the process output data, and the operation reference mode. After the parameter calculation step, before selecting The PID parameter group corresponding to one of the error indicators is used to control the PID controller to control the process of the process equipment.

It can be seen from the above description that the process control method of the embodiment of the present invention does not need to identify the process mode to design the PID controller, and eliminates the identification process steps required by the prior art, and greatly reduces the time required to design the PID controller.

R(s)‧‧‧Setpoint data

G _C (s)‧‧‧PID controller

U(s)‧‧‧ Process input data

D(s)‧‧‧External interference

G(s)‧‧‧Processing procedures

Y(s)‧‧‧ process output data

T(s)‧‧‧ operational reference mode

300‧‧‧Process Control Method

310~340‧‧‧Steps

332, 332a, 332b‧ ‧ steps

The above and other objects, features, and advantages of the present invention will become more apparent and understood. A block diagram of a feedback control system in accordance with an embodiment of the present invention.

2 is a block diagram showing a simplified feedback control system in accordance with an embodiment of the present invention.

3 is a flow chart showing a process control method according to an embodiment of the present invention.

Figure 4 is a flow chart showing the steps of parameter calculation according to an embodiment of the present invention.

Figure 5a is a diagram showing the PID control parameters of the crude argon column pressure control system of the embodiment of the present invention.

Figure 5b shows the operation data of the crude argon column pressure control system before the PID parameters are modified.

Figure 5c is a diagram showing the operation data of the crude argon column pressure control system of the embodiment of the present invention after the PID parameters are modified.

Please refer to FIG. 1 , which is a block diagram of a feedback control system according to an embodiment of the present invention, wherein R(s) represents setpoint data; G _C (s) represents a PID controller: U(s) is a process. Input data; D(s) stands for external interference; G(s) stands for process procedure; Y(s) is process output data. In order to directly perform PID control parameter tuning by using the process input data and the process output data, the embodiment of the present invention describes the desired regression control system set point response by specifying the process reference mode T(s) of the process, such as Figure 2 shows.

As can be seen from the block diagram shown in Fig. 2, the controller parameter tuning problem can be converted into the problem of minimizing the error between the Û ( s ) calculated by the following equation (1) and the real measurement U ( s ): Where K _C , τ ₁ , τ _D are PID parameters.

In an embodiment of the invention, the operational reference mode T(s) describes the dynamic behavior of the control system desired by the designer of the PID controller, ie, the design goals of the PID controller. Embodiments of the present invention provide three different reference modes for user selection according to three different control targets, which are corresponding to servo control (setpoint tracking) design, adjustment control (interference rejection) design, and Compliant control (consistent with setpoint tracking and interference rejection) design.

First, discuss the servo control (setpoint tracking) design. When the user decides that the control target is setpoint tracking, the corresponding reference mode T _s ( s ) is as follows (2):

The θ _s of the above formula is a delay parameter, which can be regarded as the equivalent delay of the process. λ _s is an adjustable parameter that allows the user to make an appropriate trade-off between control performance and system stability toughness ( M _s value). As long as the user specifies a value of M _{s , the} value of λ _s can be determined. . λ _s can be expressed by the following formula (3): Among them, 1.2 M _s 2.0

Next, discuss the adjustment control (interference rejection) design. When the user decides that the control target is interference rejection, the corresponding reference mode T _r ( s ) is as follows (4): among them . The process parameter τ can be obtained from the ladder response of the system according to the following formula: Where K is the steady-state gain of the process and is the magnitude of the input step change for the h process.

The θ _r of the above formula is a delay parameter, which can be regarded as the equivalent delay of the process. λ _r is an adjustable parameter that allows the user to make an appropriate trade-off between control performance and system stability toughness ( M _s value). As long as the user specifies a value of M _{s , the} value of λ _r can be determined. . λ _r can be expressed by the following formula (5): among them, Among them, 1.2 M _s 2.0, 0.05 ( θ _r / τ ) 1.

Next, discuss the design of the compromise control (both set point tracking and interference elimination). When the user decides that the control target is to balance setpoint tracking and interference elimination, the corresponding reference mode T _i ( s ) is as follows (6): Where α _i = (1- w ) α _s + wα _r ; 0 < w <1; α _s = λ _s .

The above formula w is a weighting factor, and the user can specify according to the importance of servo control and adjustment control. The larger the W value, the more important the adjustment control is. Θi _i is a delay parameter, which can be regarded as the equivalent delay of the process. λ _i is an adjustable parameter that allows the user to make an appropriate trade-off between control performance and system stability toughness ( M _s value). As long as the user specifies a value of M _{s , the} value of λ _i can be determined. . λ _i can be expressed by the following formula (7): Among them, 1.2 M _s 2.0

Please refer to FIG. 3 , which is a schematic flowchart diagram of a process control method 300 according to an embodiment of the present invention. The process control method 300 applies the above reference mode to perform PID control on a process device of a process. In the process control method 300, step 310 is first performed to obtain a plurality of process input data U(t) and a plurality of process output data Y(t) of the process device. The process input data U(t) and the process output data Y(t) are converted into the aforementioned process input data U(s) and the process output data Y(s) after being converted by the S domain. Next, step 320, to be T from the three reference patterns according to the user's design goals _{s (s),} the T _r (s) and T _i (s) are selected as a process operation of the reference pattern T (S ). Then, step 330 is performed to calculate a plurality of PID parameter groups of the PID controller (each parameter group includes K _C , τ ₁ , τ _D ) and a plurality of corresponding PID parameter groups by using the above formula (1). Error indicator J (θ). In an embodiment of the present invention, step 330 performs an iterative operation to obtain the PID parameter set and the error index J(θ) through a plurality of parameter calculation steps, and each of the parameter calculation steps performed is applied. Different delay parameter values until convergence (ie find the smallest J(θ)).

Please refer to FIG. 4, which is a schematic flowchart diagram of a parameter calculation step 332 according to an embodiment of the present invention. In parameter calculation step 332, step 332a is first performed to give the value of the time delay parameter. Next, step 332b is performed to calculate the PID parameter group of the PID controller by using the time delay parameter, the foregoing process input data, the foregoing process output data, and the operation reference mode selected in step 320 by inserting the above formula (1). Corresponding error indicator J(θ). For example, after the delay parameter, the process input data, the process output data, and the operational reference mode selected in step 320 are put into the above formula (1), multiple equations can be obtained, and the error index J(θ) represents these equations. The error relationship between the two. Among them, the error index J(θ) is expressed by the following formula (8): among them And the frequency ω _M is the frequency at which the sensitivity function of the control system generates a maximum value, that is,

After step 330, step 340 is followed to select a PID parameter set corresponding to one of the error indicators J(θ) to control the PID controller to control the process of the process device accordingly. For example, step 330 performs an iterative operation to substitute different delay parameter values into the above equation (1) to repeat the parameter calculation step 332 until J(θ) converges, wherein the PID corresponding to the minimum error index J(θ) The parameter group is selected as the PID parameter of the PID controller to control the process of the process equipment.

Please refer to FIG. 5a-5c at the same time, FIG. 5a is a diagram showing PID control parameters of a crude argon column pressure control system according to an embodiment of the present invention, and FIG. 5b is a diagram showing the crude argon column pressure control system before modification of a PID parameter. Operation data, Figure 5c shows the operation data of this crude argon column pressure control system after the PID parameters are modified. It can be seen from the figure 5b-5c that when the crude argon column pressure control system applies the process control method 300 of the embodiment of the present invention to modify the PID parameter, the control error ERR value (the ratio of the set value to the output value) is significantly improved, and The average ISE (Integral Squared Error) value is improved by 50%, and the average IAE (Integral absolute Error) value is improved by 30%. Embodiment of the present invention The process control method 300 can not only provide better PID control parameters, but also does not require model identification. The time cost required for the process control method 300 of the embodiment of the present invention can be greatly reduced.

While the invention has been described above in terms of several embodiments, it is not intended to limit the scope of the invention, and the invention may be practiced in various embodiments without departing from the spirit and scope of the invention. The scope of protection of the present invention is defined by the scope of the appended claims.

300‧‧‧Process Control Method

310~340‧‧‧Steps

Claims (10)

A process control method for controlling a process device by using a Proportional-Integral-Derivative (PID) controller, wherein the process control method comprises: obtaining a plurality of process input data and a plurality of the process device Pen process output data; selecting one of the proportional-integral-derivative controllers from a plurality of reference modes to operate the reference mode; performing a plurality of parameter calculation steps to obtain a plurality of PID parameters of the proportional-integral-derivative controller And a plurality of error indicators corresponding to the group of PID parameters, wherein each of the parameter calculation steps includes: providing a value of a delay parameter; and using the delay parameter, the process input data, and the process output data And the operation reference mode to calculate one of the PID parameter groups of the proportional-integral-derivative controller and one of the error indicators; and selecting a PID parameter corresponding to one of the error indicators The group controls the proportional-integral-derivative controller to control the process of the process equipment. The process control method of claim 1, wherein the reference modes comprise a first reference mode, the first reference mode T _s ( s ) being represented by the following equation: Where θ _{s is} the value of the delay parameter; The process control method of claim 2, wherein the reference modes comprise a second reference mode, the second reference mode T _r ( s ) is represented by the following equation: Where θ _{r is} the value of the delay parameter; Where K is the steady-state gain of the process, and h is the size of the process input step change; p ₃ = 1.203 ( θ _r / τ ) ^{3 -} 1.463 ( θ _r / τ ) ² + 0.789 ( θ _r / τ ) + 0.01257 where 1.2 M _s 2.0, 0.05 ( θ _r / τ ) 1. The process control method of claim 3, wherein the reference modes comprise a third reference mode, the third reference mode T _i ( s ) being represented by the following equation: Where θ _{i is} the value of the delay parameter; α _i = (1 - w ) α _s + wα _r ; 0 < w <1; α _s = λ _s . The process control method of claim 4, wherein the three reference modes are used for tradeoff control. The process control method of claim 3, wherein the two reference modes are used for adjustment control. The process control method of claim 2, wherein the reference mode is used for servo control. The process control method of claim 1, wherein the delay parameter is a first-order delay parameter. The process control method of claim 1, wherein the value of the delay parameter used in each of the proportional-integral-differential calculation steps is different. The process control method of claim 1, wherein the step of calculating the one of the PID parameter sets of the proportional-integral-derivative controller is calculated by the following equation: Where Û ( s ) is one of the input data of the processes; T ( s ) is the operational reference mode; Y ( s ) is the process output data of the one corresponding to the input data of the processes.