An optimization method and system for PID parameters of a PID control system

Abstract

The present invention relates to an optimization method and system for PID parameters of a PID control system. The optimization method includes: collecting closed-loop control data of the current feedback control loop; respectively filtering the PV curve and the MV curve, and respectively performing polynomial fitting on the filtered PV curve and the filtered MV curve; respectively preprocessing the first fitting curve and the second fitting curve; analyzing the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys to obtain characteristic information for evaluating the control effect of the PID parameters; evaluating the control effect of the PID parameters based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameters; optimizing the proportionality and integral time in the PID parameters based on the convergence factor and the overshoot factor, so that not only continuous optimization can be achieved, but also the PID control effect can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of PID control, and particularly to an optimization method and system for PID parameters of a PID control system. Background Art

[0002] For a PID control system, the existing PID parameter optimization methods are mainly implemented by the internal model method, which obtains the dynamic response of the loop through step testing to achieve the optimization of PID parameters. Moreover, this optimization method relies on model identification. Due to the characteristics and defects of the existing identification methods, the accuracy of this method in open-loop identification is often higher than that in closed-loop identification and depends on step signals.

[0003] However, in the actual production process, considering the stability of production, most loops do not have the conditions for step testing, and the existing optimization methods are limited. Moreover, the PID parameters obtained by the existing PID optimization methods often have no relation to the current control state, and the control information under the current PID parameters cannot be fully utilized, so continuous optimization cannot be achieved. At the same time, for a small number of existing closed-loop PID parameter optimization methods, they rely on fixed strategies for qualitative analysis and optimization, and there are defects in accuracy. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the above-mentioned disadvantages and deficiencies of the prior art, the present invention provides an optimization method and system for PID parameters of a PID control system, which solves the technical problems of inability to continuously optimize and poor control effect existing in the prior art.

[0006] (II) Technical Solutions

[0007] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0008] First aspect, an embodiment of the present invention provides an optimization method for PID parameters of a PID control system. The optimization method includes: collecting closed-loop control data of the current feedback control loop; wherein the closed-loop control data includes a PV curve and an MV curve; respectively filtering the PV curve and the MV curve to obtain a filtered PV curve and a filtered MV curve, and respectively performing polynomial fitting on the filtered PV curve and the filtered MV curve to obtain a first fitting curve corresponding to the filtered PV curve and a second fitting curve corresponding to the filtered MV curve; respectively preprocessing the first fitting curve and the second fitting curve to remove abnormal peaks and valleys in the first fitting curve and abnormal peaks and valleys in the second fitting curve, obtaining a first fitting curve after removing abnormal peaks and valleys and a second fitting curve after removing abnormal peaks and valleys; analyzing the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys to obtain characteristic information for evaluating the control effect of the PID parameters; evaluating the control effect of the PID parameters based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameters; wherein the evaluation parameter includes a convergence factor and an overshoot factor; optimizing the proportionality and integral time in the PID parameters based on the convergence factor and the overshoot factor.

[0009] In a possible embodiment, the closed-loop control data further includes an SV curve; the characteristic information includes but is not limited to the absolute phase difference between the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys, the change time for the filtered PV curve to reach the change value of the SV curve starting from the change moment of the SV curve when the SV curve changes, the maximum overshoot when the SV curve changes, the average oscillation period when the SV curve does not change, and the oscillation amplitude when the SV curve does not change.

[0010] In a possible embodiment, when the SV curve changes, evaluating the control effect of the PID parameters based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameters, including: comparing the magnitude of the maximum overshoot and the allowable control deviation corresponding to the current feedback control loop; if the maximum overshoot is less than or equal to the allowable control deviation corresponding to the current feedback control loop and the maximum overshoot is not equal to 0, then calculating the time ratio of the adjustment time and the change time corresponding to the current feedback control loop, and determining the convergence factor as the convergence factor corresponding to the target ratio range based on the target ratio range where the time ratio is located; if the maximum overshoot is greater than the allowable control deviation corresponding to the current feedback control loop, then obtaining the maximum PV adjustment value of the filtered PV curve, and calculating the first ratio of the maximum PV adjustment value and the SV adjustment value, then substituting the first ratio into the formula of the overshoot factor overshoot = a - 0.5 to obtain the overshoot factor; wherein a represents the first ratio.

[0011] In a possible embodiment, when there is a change in the SV curve, the control effect of the PID parameters is evaluated based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameters, which further includes: if the maximum overshoot is equal to 0, obtain the PV adjustment value in the filtered PV curve that is closest to the SV adjustment value in the SV curve, and calculate the second ratio between the SV adjustment value and the PV adjustment value, then substitute the second ratio into the formula of the overshoot factor overshoot = b * 0.5 to obtain the overshoot factor; where b represents the second ratio.

[0012] In a possible embodiment, when there is no change in the SV curve, the control effect of the PID parameters is evaluated based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameters, including: determining that all peak amplitudes and all valley amplitudes of the filtered PV curve are within the target range; where the target range is determined by the SV adjustment value in the SV curve and the allowable control deviation corresponding to the current feedback control loop; if it is determined that there is at least one peak amplitude or valley amplitude in the filtered PV curve that exceeds the target range and at the same time the average oscillation period is greater than the steady-state threshold corresponding to the current feedback control loop, then determine the range of the absolute phase difference, and based on the range of the absolute phase difference, determine the overshoot factor, and at the same time determine the convergence factor based on the absolute phase difference and the oscillation amplitude.

[0013] In a possible embodiment, determining the range of the absolute phase difference and based on the range of the absolute phase difference includes: if the absolute phase difference is less than 18 degrees, the overshoot factor is 0.5; if the absolute phase difference is in the range of 18 - 72 degrees, substitute the absolute phase difference into the formula of the overshoot factor overshoot = (c - 18) / 108 + 0.5 to obtain the overshoot factor; where c represents the absolute phase difference; if the absolute phase difference c exceeds 72 degrees, the overshoot factor is 1.

[0014] In a possible embodiment, determining the convergence factor based on the absolute phase difference and the oscillation amplitude includes: if the absolute phase difference is in the range of 30 - 90 degrees, substitute the absolute phase difference into the formula of the convergence factor convergencespeed = (90 - c) / 120 to obtain the convergence factor; where c represents the absolute phase difference; if the absolute phase difference is less than 30 degrees, calculate the third ratio of the oscillation amplitude vmean to 2 * error and determine whether the third ratio is greater than 2; if the third ratio is less than or equal to 2, substitute the third ratio into the formula of the convergence factor convergencespeed = f / 2 to obtain the convergence factor; where f is the third ratio; if the third ratio is greater than 2, the convergence factor is 1.

[0015] In a possible embodiment, when there is a change in the SV curve, the proportionality and integral time in the PID parameters are optimized based on the convergence factor and overshoot factor, including:

[0016] If the overshoot factor is less than 0.5 and the convergence factor is less than 0.5, the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0017] ;

[0018] ;

[0019] In the formula, PB old is the proportionality before optimization; overshoot is the overshoot factor; convergencespeed is the convergence factor; Ti old is the integral time before optimization;

[0020] Or, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is less than 0.5, the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0021] ;

[0022] ;

[0023] Or, if the overshoot factor is less than 0.5 and the convergence factor is greater than 0.5, the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0024] ;

[0025] ;

[0026] Or, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is greater than or equal to 0.5, the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0027] ;

[0028] 。

[0029] In a possible embodiment, when there is no change in the SV curve, the proportionality and integral time in the PID parameters are optimized based on the convergence factor and overshoot factor, including:

[0030] If the overshoot factor is less than 0.5 and the convergence factor is less than 0.5, then the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0031] ;

[0032] ;

[0033] where, PB old is the proportionality before optimization; overshoot is the overshoot factor; convergencespeed is the convergence factor; Ti old is the integral time before optimization;

[0034] Or, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is less than 0.5, then the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0035] ;

[0036] ;

[0037] Or, if the overshoot factor is less than 0.5 and the convergence factor is greater than 0.5, then the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0038] ;

[0039] ;

[0040] Or, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is greater than or equal to 0.5, then the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0041] ;

[0042] 。

[0043] In a second aspect, an embodiment of the present invention provides a PID control system, including a controller, which is configured to execute the optimization method for the PID parameters of the PID control system according to any one of the first aspect.

[0044] (III) Beneficial effects

[0045] The beneficial effects of the present invention are as follows:

[0046] An embodiment of the present application provides an optimization method and system for the PID parameters of a PID control system. By collecting the closed-loop control data of the current feedback control loop, then filtering the PV curve and the MV curve respectively to obtain the filtered PV curve and the filtered MV curve, and performing polynomial fitting on the filtered PV curve and the filtered MV curve respectively to obtain the first fitting curve corresponding to the filtered PV curve and the second fitting curve corresponding to the filtered MV curve, then preprocessing the first fitting curve and the second fitting curve respectively to remove the abnormal peaks and valleys in the first fitting curve and the abnormal peaks and valleys in the second fitting curve, obtaining the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys, then analyzing the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys to obtain the characteristic information for evaluating the control effect of the PID parameters, then evaluating the control effect of the PID parameters based on the characteristic information to obtain the evaluation parameters of the control effect of the PID parameters, where the evaluation parameters include a convergence factor and an overshoot factor, and then optimizing the proportionality and integral time in the PID parameters based on the convergence factor and the overshoot factor. Compared with the existing solutions, it can not only achieve continuous optimization, but also improve the PID control effect.

[0047] To make the above objects, features, and advantages to be achieved by the embodiments of the present application more obvious and understandable, the following specifically illustrates preferred embodiments in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 A flow chart of a method for optimizing PID parameters of a PID control system provided in an embodiment of the present application is shown;

[0050] Figure 2 A schematic diagram of a PID control system provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0052] In order to solve the problem of poor control effect and stability of PID control loops in the prior art, an embodiment of the present application provides a method and system for optimizing PID parameters of a PID control system, which evaluates the PID control effect with the help of relevant information such as loop type characteristics and control trends under current PID parameter control, and completes PID parameter optimization based on the evaluation results. Since the PID parameter optimization method is based on existing PID parameters, the method can support continuous iterative optimization of closed-loop loops, gradually improve the PID control effect, and can also lower the threshold for PID parameter optimization and improve the control effect and stability of the PID control loop.

[0053] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0054] In order to facilitate understanding of the embodiments of the present application, some terms involved in the present application are first explained as follows:

[0055] “PV”: measured value;

[0056] “SV”: set value;

[0057] "MV": valve position value;

[0058] "Average time interval tmean": It represents the mean of the time between all adjacent peaks and troughs of the same curve.

[0059] It should be noted here that in the calculation process of the average time interval tmean, half-wave units are used (0 degrees and 180 degrees of trigonometric functions, not 360 degrees). This calculation method is relatively more accurate than the calculation method based on 360 degrees. Of course, calculation based on 360 degrees is also possible;

[0060] "Average amplitude vmean": It represents the average value of the amplitudes between all the peaks and valleys of the same curve;

[0061] "Time interval t1": It represents the time interval between the current adjacent peak and valley of the same curve;

[0062] "Amplitude v1": It represents the amplitude between the current adjacent peak and valley of the same curve;

[0063] "Waveform error time tmin": For different types of feedback control loops, the waveform error time tmin is different. Specifically, refer to Table 1 mentioned later;

[0064] "Change time tup": It represents the time when PV reaches the change value of the SV curve starting from the change moment of the SV curve when there is a change in the SV curve;

[0065] "Maximum overshoot overmax": It represents the maximum overshoot of PV when there is a change in the SV curve;

[0066] "Allowable control deviation error": For different types of feedback control loops, the allowable control deviation error is different. Specifically, refer to Table 1 mentioned later;

[0067] "Adjustment time adjusttime": For different types of feedback control loops, the adjustment time adjusttime is different. Specifically, refer to Table 1 mentioned later;

[0068] "Steady-state threshold stabletime": For different types of feedback control loops, the steady-state threshold stabletime is different. Specifically, refer to Table 1 mentioned later;

[0069] "Convergence factor convergencespeed": It is used to evaluate the current control effect and for PID parameter optimization;

[0070] "Overshoot factor overshoot": It is used to evaluate the current control effect and for PID parameter optimization;

[0071] "PB": Proportional band;

[0072] "TI": Integral time.

[0073] The first embodiment

[0074] Please refer to Figure 1 , Figure 1 shows a flowchart of an optimization method for PID parameters of a PID control system provided by an embodiment of the present application. As Figure 1As shown, the optimization method can be executed by an electronic device, and the specific device of the electronic device can be set according to actual needs. The embodiments of the present application are not limited thereto. For example, the electronic device can be a computer or a server, etc. Specifically, the optimization method includes:

[0075] Step S110, collect the closed-loop control data of the current feedback control loop. Among them, the closed-loop control data includes a PV curve, an MV curve, and an SV curve.

[0076] It should be understood that the type of the current feedback control loop can be set according to actual needs. The embodiments of the present application are not limited thereto.

[0077] For example, the type of the current feedback control loop can be a flow loop, a pressure loop, a liquid level loop, a temperature loop, or other loops.

[0078] Step S120, filter the PV curve and the MV curve respectively to obtain the filtered PV curve and the filtered MV curve, and perform polynomial fitting on the filtered PV curve and the filtered MV curve respectively to obtain the first fitting curve corresponding to the filtered PV curve and the second fitting curve corresponding to the filtered MV curve.

[0079] Specifically, in order to better find the characteristics of the control curve, the first-order mean filtering method can be used to filter the PV curve and the MV curve respectively to obtain the filtered PV curve and the filtered MV curve, perform polynomial fitting on the filtered PV curve to obtain the first fitting curve, and perform polynomial fitting on the filtered MV curve to obtain the second fitting curve.

[0080] Step S130, preprocess the first fitting curve and the second fitting curve respectively to remove the abnormal peaks and valleys in the first fitting curve and the abnormal peaks and valleys in the second fitting curve, and obtain the first fitting curve after removing the abnormal peaks and valleys and the second fitting curve after removing the abnormal peaks and valleys.

[0081] Specifically, determine all the peak and valley information in the first fitting curve, and then calculate the average time interval tmean and the average amplitude vmean of all the peaks and valleys in the first fitting curve. Also, calculate the time interval t1 and the amplitude v1 between the current adjacent peaks and valleys in the first fitting curve. When satisfying and then determine the current adjacent peaks and valleys as abnormal peaks and valleys, and remove the current adjacent peaks and valleys.

[0082] In addition, if t1 < tmin is satisfied, it is also determined that the currently adjacent peak and valley are abnormal peak and valley, and the currently adjacent peak and valley are removed. Among them, for different types of feedback control loops, the waveform error time tmin is also different. Specifically, please refer to Table 1 below.

[0083] Table 1: Loop Parameter Selection Table

[0084]

[0085] As shown in Table 1, when the type of the current feedback control loop is a flow loop, the waveform error time tmin is 1 minute; when the type of the current feedback control loop is a pressure loop or a liquid level loop or a temperature loop, the waveform error time tmin is 5 minutes; when the type of the current feedback control loop is other loops, the waveform error time tmin is 3 minutes.

[0086] In addition, for the adjustment time adjusttime, the steady-state threshold stabletime, and the allowable control deviation error in Table 1, they are similar to the waveform error time tmin, and the corresponding values can be selected according to the type of the feedback control loop mentioned in Table 1, which will not be repeated here.

[0087] It should be noted here that although multiple values are recorded in Table 1, those skilled in the art should understand that they can adjust the values in Table 1 according to actual needs, and those skilled in the art are not limited to this. For example, although the waveform error time tmin corresponding to the flow loop recorded in Table 1 is 1 minute, those skilled in the art can adjust it to 2 minutes, etc. according to actual needs.

[0088] It should be noted here that in the preprocessing process of the first fitting curve, although it is described by taking the coefficient 1 / 4 as an example, those skilled in the art should understand that the coefficient can also be adjusted to other coefficients, as long as the coefficient is greater than 0 and less than 0.5. The embodiments of the present application are not limited to this.

[0089] It should also be noted here that the preprocessing process of the second fitting curve is similar to that of the first fitting curve, which will not be repeated here. Specifically, please refer to the relevant description of the first fitting curve.

[0090] Step S140, analyze the first fitting curve after removing abnormal peak and valley and the second fitting curve after removing abnormal peak and valley to obtain characteristic information for evaluating the control effect of PID parameters.

[0091] It should be understood that the specific information included in the characteristic information can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0092] For example, the characteristic information includes, but is not limited to, the absolute phase difference between the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys, the change time for the filtered PV curve to reach the change value of the SV curve starting from the change moment of the SV curve when the SV curve changes, the maximum overshoot overmax when the SV curve changes, the average oscillation period when the SV curve does not change, and the oscillation amplitude when the SV curve does not change.

[0093] It should also be understood that the process of obtaining the characteristic information can be set according to actual requirements, and the embodiments of the present application are not limited thereto.

[0094] Optionally, the process of obtaining the absolute phase difference between the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys is as follows:

[0095] The peaks and valleys of the first fitting curve after removing abnormal peaks and valleys can be sequentially corresponding to the peaks and valleys of the second fitting curve after removing abnormal peaks and valleys. At the same time, if there are peaks and valleys that cannot be corresponding, they can be deleted. For example, the first peak of the first fitting curve after removing abnormal peaks and valleys is corresponding to the first peak of the second fitting curve after removing abnormal peaks and valleys, and the first valley of the first fitting curve after removing abnormal peaks and valleys is corresponding to the first valley of the second fitting curve after removing abnormal peaks and valleys, etc. Subsequently, the absolute phase difference between the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys can be calculated, and the value range of this absolute phase difference is 0 to 90°, that is, it calculates the absolute phase difference of the corresponding peaks and valleys in the first fitting curve after removing abnormal peaks and valleys and the second fitting curve after removing abnormal peaks and valleys according to the above corresponding method.

[0096] Optionally, the process of obtaining the change time tup for the filtered PV curve to reach the change value of the SV curve starting from the change moment of the SV curve and the maximum overshoot overmax when the SV curve changes is as follows:

[0097] When the SV curve changes (that is, the SV curve is not a straight line), the number of changes of the SV curve can be determined.

[0098] Among them, when the number of changes in the SV curve is one (that is, at this time, the SV curve includes two parallel straight lines, and there is a connecting line between the two straight lines), the change time tup from the change moment of the filtered PV curve to the change value of the SV curve (that is, the moment corresponding to the connection point of the first straight line and the connecting line among the two parallel straight lines at this time) can be calculated, and the maximum overshoot overmax can be calculated;

[0099] When the number of changes in the SV curve is multiple, multiple change times tup from the change moment of the filtered PV curve to the corresponding change value of the SV curve each time can be calculated. For example, when the number of changes in the SV curve is two, the first change time tup from the first change moment of the filtered PV curve to the first change value of the SV curve can be calculated, and the second change time tup from the second change moment of the filtered PV curve to the second change value of the SV curve can also be calculated. Subsequently, the average value of the multiple change times tup can be calculated, and this average value can be used as the final change time tup, and the maximum overshoot overmax can be calculated.

[0100] It should be understood that the maximum overshoot overmax is the amount by which the adjustment exceeds the SV adjustment value. For example, when SV changes from 10 to 15, under the action of the controller, PV rises from 10, reaches 15, and may continue to rise to 16 and then fall back to around 15. At this time, the overshoot is 16 - 15 = 1. The same is true for downward adjustment. When SV changes from 10 to 5, PV drops to 4 at the lowest, and the overshoot at this time is 1.

[0101] Here, it should be noted that in principle, only the data segment with one change in SV is analyzed during the analysis (one segment of analysis data contains at most one change in SV), but this method also supports being used in the case of multiple changes in SV. And, in the case of multiple changes, the maximum overshoot overmax can be calculated separately, and finally, the average value is taken (when calculating the overshoot factor overshoot, this factor also needs to be calculated separately and then the average value is taken). This will be described later.

[0102] Optionally, the process of obtaining the average oscillation period and the oscillation amplitude when the SV curve has no change is as follows:

[0103] When the SV curve has no change (that is, the SV curve is a straight line), the average oscillation period is 2*tmean, and the oscillation amplitude is vmean.

[0104] It should be noted here that when calculating the average time interval tmean previously, for more accurate calculation, actually half of a wave was calculated, that is, the time interval from the PV peak to the trough (i.e., the positions of 0 degrees and 180 degrees of the trigonometric function, not 360 degrees). Therefore, to obtain a complete oscillation period, this value needs to be multiplied by 2. In addition, if calculated according to 360 degrees, there is no need to multiply by 2.

[0105] Step 150: Evaluate the control effect of the PID parameters based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameters. Among them, the evaluation parameter includes a convergence factor and an overshoot factor.

[0106] It should be understood that the specific process of evaluating the control effect of the PID parameters based on the characteristic information can be set according to actual needs, and the embodiments of the present application are not limited thereto.

[0107] Optionally, when there is a change in the SV curve, since the allowable control deviation error corresponding to different types of feedback control loops is different, the allowable control deviation error corresponding to the current feedback control loop can be determined by querying Table 1. And the maximum overshoot overmax and the allowable control deviation error corresponding to the current feedback control loop can be compared.

[0108] Among them, if the maximum overshoot overmax is less than or equal to the allowable control deviation error corresponding to the current feedback control loop and the maximum overshoot overmax is not equal to 0, then calculate the time ratio of the adjustment time adjusttime and the change time tup corresponding to the current feedback control loop (i.e., adjusttime / tup), and determine the convergence factor as the convergence factor corresponding to the target ratio range based on the target ratio range where the time ratio is located.

[0109] For example, when the maximum overshoot overmax is less than or equal to the allowable control deviation error corresponding to the current feedback control loop and the maximum overshoot overmax is not equal to 0, calculate the time ratio t of the adjustment time adjusttime and the change time tup corresponding to the current feedback control loop. Subsequently, if it is determined that the time ratio t is within the range of 0.5 - 0.9, the convergence speed is too slow, and at this time the convergence factor convergencespeed = (t - 0.5) * 1.25; if it is determined that the time ratio t is less than 0.5, the convergence factor convergencespeed is 0 at this time; if it is determined that the time ratio t is within the range of 1.1 - 1.5, the convergence factor convergencespeed = (t - 1.1) * 1.25 + 0.5; if it is determined that the time ratio t is greater than 1.5, the time ratio t is processed as 1.5 at this time, so the convergence factor convergencespeed = (1.5 - 1.1) * 1.25 + 0.5 = 1.

[0110] In addition, if the maximum overshoot overmax is equal to 0, that is, there is no overshoot, it indicates that there is an under-adjustment in the loop and the adjustment speed is too slow. At this time, obtain the PV adjustment value closest to the SV adjustment value in the SV curve in the filtered PV curve, and calculate the second ratio b of the SV adjustment value and the PV adjustment value. And the second ratio b must be between 0 and 1, then the overshoot factor overshoot = b * 0.5. For example, assume that the current PV and SV are both 10. Subsequently, adjust SV to 15, and then PV slowly rises to a maximum of 13. At this time, the second ratio b here is (13 - 10) / (15 - 10) = 0.6, then the overshoot factor overshoot = 0.6 * 0.5 = 0.3.

[0111] It should be noted here that if SV is adjusted multiple times, the overshoot factor overshoot can be obtained in the above manner for each adjustment of SV, and then the average value of multiple overshoot factors overshoot is taken as the final overshoot factor overshoot.

[0112] Correspondingly, other situations are similar. Specifically, in the case where SV is adjusted multiple times, it is also possible

[0113] In addition, when the maximum overshoot overmax is greater than the allowable control deviation error corresponding to the current feedback control loop, there is overshoot in the loop. Obtain the maximum PV adjustment value of the filtered PV curve, and calculate the first ratio a of the maximum PV adjustment value and the SV adjustment value. Then, the overshoot factor overshoot = a - 0.5. For example, when SV changes from 10 to 15, under the action of the controller, PV rises from 10, reaches 15, and may continue to rise to 18 and then fall back to around 15. Thus, the maximum PV adjustment value is 18, the first ratio a = 1.2, and further the overshoot factor overshoot = 1.2 - 0.5 = 0.7.

[0114] Optionally, when there is no change in the SV curve, it is determined whether all peak amplitudes and all valley amplitudes of the filtered PV curve are within the target range of [SV adjustment value - allowable control deviation error, SV adjustment value + allowable control deviation error]. If there is at least one peak amplitude or at least one valley amplitude exceeding the range of [SV adjustment value - allowable control deviation error, SV adjustment value + allowable control deviation error], the maximum overshoot overmax and the convergence factor convergencespeed are determined by the oscillation conditions of PV and MV. Specifically:

[0115] For the case where the response of the filtered PV curve to the filtered MV curve has a lag, it is determined as overshoot, and the lag can be represented by the absolute phase difference c (unit: degree). If the absolute phase difference c is less than 18 degrees, the overshoot factor overshoot = 0.5, that is, there is no overshoot; if the absolute phase difference c is in the range of 18 - 72 degrees, the overshoot factor overshoot = (c - 18) / 108 + 0.5; if the absolute phase difference c exceeds 72 degrees, the overshoot factor overshoot is equal to 1.

[0116] Meanwhile, to cope with the lag, PID parameter optimization can also be synchronously performed by accelerating convergence. If the average oscillation period is greater than the steady-state threshold stabletime of the corresponding loop, as shown in Table 1 specifically, for the loop with the absolute phase difference in the range of 30 - 90 degrees, the convergence factor convergencespeed = (90 - c) / 120. Also, for the loop with the absolute phase difference less than 30 degrees and at least one peak amplitude or at least one valley amplitude outside the range of [SV adjustment value - allowable control deviation error, SV adjustment value + allowable control deviation error], it is regarded as oscillation caused by too fast convergence, and the third ratio of the oscillation amplitude vmean and 2*error can be calculated f and the convergence factor convergencespeed = the third ratio f / 2. Meanwhile, if the ratio f is greater than 2, the ratiof Regarded as 2, that is, when the ratio f is greater than 2, the convergence factor convergencespeed = 1.

[0117] Step S160, optimize the proportionality and integral time in the PID parameters based on the convergence factor and overshoot factor.

[0118] It should be noted here that in step S150, in some cases, both (i.e., the convergence factor and overshoot factor) are calculated and evaluated, and in some cases, only one of the indicators (i.e., the convergence factor or overshoot factor) is calculated and evaluated. In the latter case, the other indicator that does not participate in the calculation and evaluation can be the default value 0.5.

[0119] It should be understood that the specific process of optimizing the proportionality and integral time in the PID parameters based on the convergence factor and overshoot factor can be set according to actual needs, and the embodiments of the present application are not limited thereto.

[0120] Optionally, when the SV curve changes, it can be divided into the following four cases:

[0121] When the overshoot factor overshoot is less than 0.5 and the convergence factor convergencespeed is less than 0.5, the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0122] ;

[0123] ;

[0124] In the formula, PB old is the proportionality before optimization; Ti old is the integral time before optimization.

[0125] And, when the overshoot factor overshoot is greater than or equal to 0.5 and the convergence factor convergencespeed is less than 0.5, the optimized proportionality PB new and the optimized integral time Ti new are calculated as follows:

[0126] ;

[0127] .

[0128] And, when the overshoot factor is less than 0.5 and the convergence speed factor is greater than 0.5, the optimized proportional band PB new and the optimized integral time Ti new are calculated as follows:

[0129] ;

[0130] .

[0131] And, when the overshoot factor is greater than or equal to 0.5 and the convergence speed factor is greater than or equal to 0.5, the optimized proportional band PB new and the optimized integral time Ti new are calculated as follows:

[0132] ;

[0133] .

[0134] Optionally, when there is no change in the SV curve, it can be divided into the following four cases:

[0135] And, when the overshoot factor is less than 0.5 and the convergence speed factor is less than 0.5, the optimized proportional band PB new and the optimized integral time Ti new are calculated as follows:

[0136] ;

[0137] .

[0138] And, when the overshoot factor is greater than or equal to 0.5 and the convergence speed factor is less than 0.5, the optimized proportional band PB new and the optimized integral time Ti new are calculated as follows:

[0139] ;

[0140] .

[0141] And, when the overshoot factor is less than 0.5 and the convergence speed is greater than 0.5, the optimized proportional band PB new and the optimized integral time Ti new are calculated as follows:

[0142] ;

[0143] .

[0144] And, when the overshoot factor is greater than or equal to 0.5 and the convergence speed is greater than or equal to 0.5, the optimized proportional band PB new and the optimized integral time Ti new are calculated as follows:

[0145] ;

[0146] .

[0147] Therefore, by means of the above technical solution, the optimization method of the PID parameters of the PID control system of the present application has the following specific advantages:

[0148] Compared with the common internal model optimization method, the present invention does not depend on step information and will not affect the on-site production process;

[0149] There is no need to build a model, and it does not depend on the model accuracy. The method of data analysis is used for PID optimization, making full use of information such as loop type characteristics and current PID parameters;

[0150] The PID parameter optimization in the present invention is obtained based on the existing PID parameters and their control effects. The optimized PID parameters have a dependency relationship with the PID parameters before optimization, making full use of the information of the current state of the loop, and will not generate a new PID parameter out of thin air. There will be no large-scale changes in the PID parameters during the optimization process, ensuring the safety of the optimization process;

[0151] The optimization process of the present invention is divided into two major parts, namely evaluation and optimization. The optimization process can be decoupled, and experienced users can add their own understanding between evaluation and optimization, which is convenient and flexible. That is to say, here the engineer's own understanding of the curve is used to replace steps S110 to S150. The whole method is divided into two processes: evaluation and tuning. Steps S110 to S150 are the evaluation process. The complete process from steps S110 to S160 can achieve one-key operation, and complete two things: evaluation and tuning (the logic is sequential, but for the user experience, it is presented all at once). The evaluation of the curve shown in the result can also be used as a reference for the engineer. The engineer can modify it and perform tuning again. That is, the following steps can be added between steps S150 and S160: when the evaluation parameters are obtained, display the evaluation parameters to the user through the interface; if a modification instruction from the user is received, modify the evaluation parameters accordingly; if a confirmation instruction from the user is received, execute step S160.

[0152] It should be understood that the method for optimizing the PID parameters of the above PID control system is only exemplary. Those skilled in the art can make various deformations according to the above method, and the deformed solutions also fall within the protection scope of this application.

[0153] Second Embodiment

[0154] Please refer to Figure 2 ,

[0155] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0156] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0157] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions.

[0158] It should be noted that the words "a" or "an" preceding a component do not exclude the existence of a plurality of such components. The present invention can be implemented by means of hardware including several different components and by means of a suitably programmed computer. In the case of several devices recited, several of these devices can be embodied by the same piece of hardware. The use of the terms first, second, third, etc. is only for convenience of description and does not denote any order. These terms can be construed as part of the component name.

[0159] In addition, it should be noted that in the description of this specification, the descriptions of terms such as "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples", etc., mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0160] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concepts. Therefore, the technical solutions should be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0161] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the technical solutions of the present invention and their equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. A method for optimizing PID parameters of a PID control system, characterized in that: include: Collecting closed-loop control data of the current feedback control loop; wherein the closed-loop control data includes a PV curve and a MV curve; filtering the PV curve and the MV curve respectively to obtain a filtered PV curve and a filtered MV curve, and performing polynomial fitting on the filtered PV curve and the filtered MV curve respectively to obtain a first fitting curve corresponding to the filtered PV curve and a second fitting curve corresponding to the filtered MV curve; Preprocessing the first fitting curve and the second fitting curve respectively to remove abnormal peaks and troughs in the first fitting curve and abnormal peaks and troughs in the second fitting curve, to obtain a first fitting curve after the abnormal peaks and troughs are removed and a second fitting curve after the abnormal peaks and troughs are removed; Analyzing the first fitting curve after the abnormal peaks and troughs are removed and the second fitting curve after the abnormal peaks and troughs are removed to obtain characteristic information for evaluating the control effect of the PID parameters; Based on the characteristic information, the control effect of the PID parameter is evaluated to obtain evaluation parameters of the control effect of the PID parameter; wherein the evaluation parameters include a convergence factor and an overshoot factor; Optimizing the proportionality and integral time in the PID parameters based on the convergence factor and the overshoot factor; The closed-loop control data further includes an SV curve; the characteristic information includes an absolute phase difference between a first fitting curve after the abnormal peaks and troughs are removed and a second fitting curve after the abnormal peaks and troughs are removed, a change time for the filtered PV curve to reach a change value of the SV curve from the change moment of the SV curve when the SV curve changes, a maximum overshoot when the SV curve changes, an average oscillation period when the SV curve does not change, and an oscillation amplitude when the SV curve does not change; When the SV curve does not change, optimizing the proportionality and integral time in the PID parameters based on the convergence factor and the overshoot factor includes: If the overshoot factor is less than 0.5 and the convergence factor is less than 0.5, the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； In the formula, PB old is the proportionality before optimization; overshoot is the overshoot factor; convergencespeed is the convergence factor; Ti old is the integration time before optimization; Alternatively, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is less than 0.5, then the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； Alternatively, if the overshoot factor is less than 0.5 and the convergence factor is greater than 0.5, the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； Alternatively, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is greater than or equal to 0.5, then the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； When the SV curve changes, the optimization of the proportionality and the integral time in the PID parameters based on the convergence factor and the overshoot factor includes: If the overshoot factor is less than 0.5 and the convergence factor is less than 0.5, the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； In the formula, PB old is the proportionality before optimization; overshoot is the overshoot factor; convergencespeed is the convergence factor; Ti old is the integration time before optimization; Alternatively, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is less than 0.5, then the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； Alternatively, if the overshoot factor is less than 0.5 and the convergence factor is greater than 0.5, the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； ； Alternatively, if the overshoot factor is greater than or equal to 0.5 and the convergence factor is greater than or equal to 0.5, then the optimized proportionality PB new and the optimized integration time Ti new The calculation formula is as follows: ； 。 2. The optimization method according to claim 1, characterized in that: When the SV curve changes, the control effect of the PID parameter is evaluated based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameter, including: Comparing the maximum overshoot with the allowable control deviation corresponding to the current feedback control loop; If the maximum overshoot is less than or equal to the allowable control deviation corresponding to the current feedback control loop and the maximum overshoot is not equal to 0, then calculating the time ratio of the adjustment time and the change time corresponding to the current feedback control loop, and based on the target ratio range in which the time ratio is located, determining the convergence factor to be a convergence factor corresponding to the target ratio range; If the maximum overshoot is greater than the allowable control deviation corresponding to the current feedback control loop, the PV maximum adjustment value of the filtered PV curve is obtained, and a first ratio of the PV maximum adjustment value to the SV adjustment value is calculated, and the first ratio is substituted into the formula of overshoot factor overshoot=a-0.5 to obtain the overshoot factor; wherein a represents the first ratio, and a=the PV maximum adjustment value / SV adjustment value.

3. The optimization method according to claim 2, characterized in that: When the SV curve changes, evaluating the control effect of the PID parameter based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameter further includes: If the maximum overshoot is equal to 0, the PV adjustment value in the filtered PV curve that is closest to the SV adjustment value in the SV curve is obtained, and a second ratio between the SV adjustment value and the PV adjustment value is calculated, and the second ratio is substituted into the formula of overshoot factor overshoot=b*0.5 to obtain the overshoot factor; wherein b represents the second ratio, and b=the PV adjustment value / the SV adjustment value.

4. The optimization method according to claim 1, characterized in that: When the SV curve does not change, the control effect of the PID parameter is evaluated based on the characteristic information to obtain an evaluation parameter of the control effect of the PID parameter, including: Determining that all peak amplitudes and all trough amplitudes of the filtered PV curve are within a target range; wherein the target range is determined by the SV adjustment value in the SV curve and the allowable control deviation corresponding to the current feedback control loop; If it is determined that there is at least one peak amplitude or trough amplitude in the filtered PV curve that exceeds the target range and at the same time the average oscillation period is greater than the steady-state threshold corresponding to the current feedback control loop, the range of the absolute phase difference is determined, and based on the range of the absolute phase difference, the overshoot factor is determined, and at the same time, the convergence factor is determined based on the absolute phase difference and the oscillation amplitude.

5. The optimization method according to claim 4, characterized in that: The determining the range of the absolute phase difference, based on the range of the absolute phase difference, comprises: If the absolute phase difference is less than 18 degrees, the overshoot factor is 0.5; If the absolute phase difference is within the range of 18-72 degrees, the absolute phase difference is substituted into the formula of overshoot factor overshoot=(c-18) / 108+0.5 to obtain the overshoot factor; wherein c represents the absolute phase difference; If the absolute phase difference c exceeds 72 degrees, the overshoot factor is 1.

6. The optimization method according to claim 4, characterized in that: The determining of the convergence factor based on the absolute phase difference and the oscillation amplitude comprises: If the absolute phase difference is between 30 and 90 degrees, the absolute phase difference is substituted into the formula of convergence speed = (90-c) / 120 to obtain the convergence factor; wherein c represents the absolute phase difference; If the absolute phase difference is less than 30 degrees, a third ratio of the oscillation amplitude vmean to 2*allowable control deviation error is calculated, and it is determined whether the third ratio is greater than 2; wherein the third ratio=vmean / 2*error, and different types of feedback control loops correspond to different allowable control deviation errors; If the third ratio is less than or equal to 2, the third ratio is substituted into the convergence speed = f / 2, the convergence factor is obtained; where, f is the third ratio; If the third ratio is greater than 2, the convergence factor is 1.

7. A PID control system, characterized in that: It comprises a controller, wherein the controller is used to execute the optimization method of PID parameters of the PID control system according to any one of claims 1 to 6.

Images