CN111766777B - PID controller and PID control method - Google Patents

PID controller and PID control method Download PDF

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CN111766777B
CN111766777B CN202010750804.9A CN202010750804A CN111766777B CN 111766777 B CN111766777 B CN 111766777B CN 202010750804 A CN202010750804 A CN 202010750804A CN 111766777 B CN111766777 B CN 111766777B
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CN111766777A (en
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赵潮
刘家国
仝晓杰
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Beijing Institute of Environmental Features
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    • G05—CONTROLLING; REGULATING
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    • 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
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Abstract

The invention relates to a PID controller and a control method, wherein the PID controller comprises: an error calculation unit for calculating an error value based on the input controlled amount and the output feedback amount; an error change rate calculation unit for calculating an error change rate from the error value; the fuzzy control unit is used for receiving the error value and the error change rate, carrying out self-adaptive setting on the PID parameters of the PID controller by utilizing a fuzzy rule, and outputting the change quantity of the PID parameters; the expert control unit is used for receiving the error value and the error change rate and obtaining an initial value of the PID parameter by utilizing an expert knowledge base; and the PID control unit is used for obtaining PID parameter values according to the initial values and the variation of the PID parameters during each PID calculation and calculating control output quantity to the controlled object according to the PID parameter values. According to the invention, the dual setting of the PID parameters is realized through expert control, fuzzy control and a PID controller, so that the control precision and performance of the PID controller are improved.

Description

PID controller and PID control method
Technical Field
The invention relates to the field of electric automation control, in particular to a PID controller and a PID control method.
Background
PID control is a relatively mature control method applied to equipment control and automatic production at present, and has the advantages of relatively simple algorithm, high stability and good robustness. Along with the development of industrial technology, the control precision requirement of the servo motor is continuously improved, and the common PID controller is difficult to meet the high-precision index.
The fuzzy self-adaptive PID algorithm is mainly formed by combining a fuzzy controller and a PID controller, wherein the fuzzy controller takes an error value e and related characteristic quantity thereof as input, and utilizes a fuzzy rule to carry out self-adaptive adjustment on PID controller parameters, namely a proportional adjustment coefficient Kp, an integral adjustment coefficient Ki and a differential adjustment coefficient Kd, the output of the fuzzy PID controller is the variation of the PID parameters, namely the variation delta Kp of the proportional adjustment coefficient, the variation delta Ki of the integral adjustment coefficient and the variation delta Kd of the differential adjustment coefficient, when the fuzzy PID controller is used, a certain initial value is required to be applied to the fuzzy controller, the current initial value is determined by adopting experience trial and error, when the initial value is selected inappropriately, the adjustment effect is difficult to achieve, and the initial value cannot be changed after the initial value is selected.
Disclosure of Invention
Aiming at the defect that the initial value determination in the fuzzy self-adaptive PID algorithm in the prior art adopts empirical trial and error and is difficult to achieve the setting effect, the invention provides a PID controller and a PID control method, wherein expert control is introduced on the basis of the fuzzy PID controller to set the parameter initial value of the fuzzy PID.
In order to solve the above technical problem, the present invention provides a PID controller, preferably comprising:
an error calculation unit for calculating an error value based on the input controlled amount and the output feedback amount;
an error change rate calculation unit for calculating an error change rate from the error value;
the fuzzy control unit is connected with the error calculation unit and the error change rate calculation unit and is used for receiving the error value and the error change rate, adaptively setting the PID parameters of the PID controller by utilizing a fuzzy rule and outputting the change quantity of the PID parameters;
the expert control unit is connected with the error calculation unit and the error change rate calculation unit and is used for receiving the error value and the error change rate and obtaining an initial value of the PID parameter by utilizing an expert knowledge base;
the PID control unit is connected with the fuzzy control unit, the expert control unit, the error calculation unit and the error change rate calculation unit and is used for obtaining a PID parameter value according to the initial value and the change amount of the PID parameter during each PID calculation and calculating the control output quantity to the controlled object according to the PID parameter value.
In the PID controller according to the present invention, preferably, the PID parameters include: proportional, integral and differential adjustment coefficients.
In the PID controller according to the present invention, preferably, the expert control unit generates initial values of the corresponding PID parameters according to the error value and the error rate of change according to the following rule mathematical model:
|e|≥ε 1 when Kp 0 ＝Kp 01 ,Ki 0 ＝Ki 01 ,Kd 0 ＝Kd 01 ；
ε 2 ≤|e|＜ε 1 When Kp 0 ＝Kp 02 ,Ki 0 ＝Ki 02 ,Kd 0 ＝Kd 02 ；
|e|＜ε 2 And |ec| is not less than delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 03 ；
|e|＜ε 2 And |ec| < delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 04
Where e is the calculated error value and ec is the calculated error rate; epsilon 1 、ε 2 Respectively are provided withA first error level value and a second error level value, delta 1 Kp is the first error variation level value 0 、Ki 0 、Kd 0 Initial values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient are respectively; kp 01 、Kp 02 、Kp 03 Respectively a preset first proportion regulating value, a preset second proportion regulating value and a preset third proportion regulating value; ki (Ki) 01 、Ki 02 、Ki 03 The first integral regulating value, the second integral regulating value and the third integral regulating value are respectively preset; kd 01 、Kd 02 、Kd 03 、Kd 04 The differential control device comprises a first differential control value, a second differential control value, a third differential control value and a fourth differential control value which are preset respectively; and Kp 01 ≈Kp 03 ＞Kp 02 ,Kd 01 ≈Kd 02 ≈Kd 03 ＜Kd 04 ,0＝Ki 01 ＜Ki 02 ＜Ki 03 ,ε 1 ＞ε 2 ＞0,δ 1 ＞0。
In the PID controller according to the present invention, preferably, the PID control unit calculates the PID parameter value by the following formula:
Kp＝Kp 0 +ΔKp
Figure BDA0002609986790000032
wherein Kp, ki and Kd are PID parameter values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively; Δkp, Δki, and Δkd are the amounts of change in the proportional adjustment coefficient, the integral adjustment coefficient, and the differential adjustment coefficient, respectively.
In the PID controller according to the present invention, preferably, the PID control unit calculates the control output by the following formula:
Figure BDA0002609986790000031
where u (k) is the control output of the kth time, e (k) is the error value calculated by the kth time, and e (j) is the error value calculated by the jth time; e (k-1) is the error value of the k-1 th calculation.
The invention also provides a PID control method, which comprises the following steps:
calculating an error value according to the input controlled quantity and the output feedback quantity;
calculating an error change rate according to the error value;
performing self-adaptive setting on PID parameters of the PID controller according to the error value and the error change rate by using a fuzzy rule, and outputting the change quantity of the PID parameters;
obtaining an initial value of the PID parameter according to the error value and the error change rate by using an expert knowledge base;
and obtaining PID parameter values according to the initial values and the variation of the PID parameters in each PID timing, and calculating control output quantity to the controlled object according to the PID parameter values.
In the PID control method according to the present invention, preferably, the PID parameters include: proportional, integral and differential adjustment coefficients.
In the PID control method according to the present invention, it is preferable that initial values of the corresponding PID parameters are generated from the error value and the error rate according to the following rule mathematical model in the method:
|e|≥ε 1 when Kp 0 ＝Kp 01 ,Ki 0 ＝Ki 01 ,Kd 0 ＝Kd 01 ；
ε 2 ≤|e|＜ε 1 When Kp 0 ＝Kp 02 ,Ki 0 ＝Ki 02 ,Kd 0 ＝Kd 02 ；
|e|＜ε 2 And |ec| is not less than delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 03 ；
|e|＜ε 2 And |ec| < delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 04
Where e is the calculated error value and ec is the calculated error rate; epsilon 1 、ε 2 A first error level value and a second error level value, delta, respectively 1 Kp is the first error variation level value 0 、Ki 0 、Kd 0 Initial values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient are respectively; kp 01 、Kp 02 、Kp 03 Respectively a preset first proportion regulating value, a preset second proportion regulating value and a preset third proportion regulating value; ki (Ki) 01 、Ki 02 、Ki 03 The first integral regulating value, the second integral regulating value and the third integral regulating value are respectively preset; kd 01 、Kd 02 、Kd 03 、Kd 04 The differential control device comprises a first differential control value, a second differential control value, a third differential control value and a fourth differential control value which are preset respectively; and Kp 01 ≈Kp 03 ＞Kp 02 ,Kd 01 ≈Kd 02 ≈Kd 03 ＜Kd 04 ,0＝Ki 01 ＜Ki 02 ＜Ki 03 ,ε 1 ＞ε 2 ＞0,δ 1 ＞0。
In the PID control method according to the present invention, preferably, the PID parameter value is calculated by the following formula:
Kp＝Kp 0 +ΔKp
Figure BDA0002609986790000041
wherein Kp, ki and Kd are PID parameter values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively; Δkp, Δki, and Δkd are the amounts of change in the proportional adjustment coefficient, the integral adjustment coefficient, and the differential adjustment coefficient, respectively.
In the PID control method according to the present invention, preferably, the control output is calculated by the following formula:
Figure BDA0002609986790000042
where u (k) is the control output of the kth time, e (k) is the error value calculated by the kth time, and e (j) is the error value calculated by the jth time; e (k-1) is the error value of the k-1 th calculation.
The PID controller and the control method thereof have the following beneficial effects: according to the invention, the dual setting of the PID parameters is realized through expert control, fuzzy control and a PID controller, so that the control precision and performance of the PID controller are improved.
Drawings
FIG. 1 is a schematic diagram of the structure of a PID controller according to a preferred embodiment of the invention;
FIG. 2 is a schematic diagram of a schematic configuration of a fuzzy control unit in a PID controller according to a preferred embodiment of the invention;
FIG. 3 is a flow chart of a PID control method according to a preferred embodiment of the invention;
FIG. 4 is a graph of error mean square error trend for four control methods of sinusoidal disturbance;
fig. 5 is an error mean square error trend graph of four control methods of step disturbance.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a schematic diagram of a PID controller according to a preferred embodiment of the present invention is shown. The PID controller of the present invention can also be referred to as an expert-fuzzy PID controller. As shown in fig. 1, the PID controller provided in this embodiment includes at least: an error calculation unit 100, an error change rate calculation unit 200, a fuzzy control unit 300, an expert control unit 400, and a PID control unit 500.
Wherein the error calculation unit 100 is configured to calculate an error value e based on the input controlled variable r and the output feedback variable y. Specifically, e=r-y. When the PID controller is started, as no output exists, the initial output feedback quantity is 0, and the difference value between the initial controlled quantity r and 0 is calculated, namely the initial error value e. After the PID controller is started, the error calculation unit 100 calculates a difference between the controlled variable r input in real time and the output feedback variable y measured in real time as an error value e calculated by the present PID. The PID controller of the invention can be applied to equipment control and automatic production, such as turntable control of azimuth rotation angle, wherein the controlled quantity r, the control output quantity u and the output feedback quantity y of the input PID controller are all azimuth rotation angles; for example, the controlled amount r is 100 °, that is, the turntable azimuth is rotated to a position of 100 °, the control output amount u given to the controlled object 600 by the PID controller is an amount calculated by the PID controller combination, for example, 99.98 °, and the output feedback amount y is an actual angle, for example, 90 °, of the turntable azimuth currently detected.
The error change rate calculation unit 200 is configured to calculate an error change rate ec from the error value e. Specifically, the first derivative of the error value e with respect to time t is calculated as the error change rate ec.
The fuzzy control unit 300 is connected with the error calculation unit 100 and the error change rate calculation unit 200, and is used for receiving the error value e and the error change rate ec, adaptively setting the PID parameters of the PID controller by using a fuzzy rule, and outputting the variation of the PID parameters. Preferably, the PID parameters include: proportional, integral and differential adjustment coefficients.
The expert control unit 400 is connected to the error calculation unit 100 and the error change rate calculation unit 200, and is configured to receive the error value e and the error change rate ec, and obtain an initial value of the PID parameter by using an expert knowledge base.
The PID control unit 500 is connected to the fuzzy control unit 300, the expert control unit 400, the error calculation unit 100, and the error rate calculation unit 200, and is configured to obtain a PID parameter value according to an initial value and a variation of a PID parameter at each PID calculation, and calculate a control output u to the controlled object 600 according to the PID parameter value. The output feedback quantity y of the controlled object is fed back to the error calculation unit 100 for the next PID operation.
In the invention, the PID parameter value adopted by the PID controller is continuously updated when each calculation is performed, which is equivalent to one judgment per round, namely, during each PID calculation, the expert control unit 400 generates the initial value of a new PID parameter according to the new error value e and the error change rate ec, the fuzzy control unit 300 generates the change quantity of the new PID parameter according to the new error value e and the error change rate ec, and the PID control unit 500 adds the two values to obtain the PID parameter value for subsequent control. Compared with the prior art that the initial value is manually preset according to the empirical value, the control precision and performance of the PID controller can be improved. Referring to fig. 2 in combination, a schematic diagram of a simulation control unit 300 in a PID controller according to a preferred embodiment of the present invention is shown. The fuzzy control unit 300 may be constructed using various fuzzy control designs well known and applicable to those skilled in the art, typically as shown in FIG. 2, including a fuzzy calculation module 310, a fuzzy rule base 320, a fuzzy inference engine 330, a defuzzification calculation module 340. The fuzzy control unit 300 uses the error value e calculated by the error calculation unit 100 and the relevant characteristic quantity of the error value as input signals, and obtains a set of variable quantities of PID parameters, namely the variable quantity delta Kp of the proportional adjustment coefficient, the variable quantity delta Ki of the integral adjustment coefficient and the variable quantity delta Kd of the differential adjustment coefficient through the processes of fuzzification, fuzzy reasoning and defuzzification. Wherein the relevant characteristic of the error value comprises a first derivative, a second derivative, and a higher derivative of the error value. I.e. the error rate of change ec, the rate of change of the error rate of change ec, etc.
The expert control unit 400 is used for determining an initial value of the PID controller, firstly, an expert knowledge base is constructed through a certain experience rule, in the expert knowledge base, error values e and error change rates ec in different ranges correspond to different sets of PID parameter values, when the PID controller works, the expert control unit 400 obtains a corresponding set of parameter values of a proportional adjustment coefficient, an integral adjustment coefficient and a differential adjustment coefficient by judging the error values e and the error change rates ec, and the set of values are used as initial values of PID control, namely, initial values Kp of the proportional adjustment coefficient 0 Initial value Ki of integral adjustment coefficient 0 And initial value Kd of differential adjustment coefficient 0 。
The basic rules of the expert knowledge base are:
(a) When |e| is large, in order for the system to have better tracking performance, a larger Kp and a smaller Kd should be taken, while in order to avoid a larger overshoot, the integration action should be limited, usually ki=0.
(b) When |e| is of medium magnitude, kp should be small in order for the system to have a small overshoot. In this case, the Kd value has a large influence on the system, and the Ki value should be smaller.
(c) When |e| is small, kp and Ki should be both large in order for the system to have good stability. Meanwhile, in order to avoid oscillation of the system at a set value and consider the anti-interference performance of the system, kd may be smaller when |ec| is larger, and Kd may be larger when |ec| is smaller.
Thus, in a more preferred embodiment of the present invention, the expert control unit 400 generates initial values of the corresponding PID parameters from the error value and the error rate of change according to the following rule mathematical model:
|e|≥ε 1 when Kp 0 ＝Kp 01 ,Ki 0 ＝Ki 01 ,Kd 0 ＝Kd 01 ；
ε 2 ≤|e|＜ε 1 When Kp 0 ＝Kp 02 ,Ki 0 ＝Ki 02 ,Kd 0 ＝Kd 02 ；
|e|＜ε 2 And |ec| is not less than delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 03 ；
|e|＜ε 2 And |ec| < delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 04
Where e is the calculated error value and ec is the calculated error rate; epsilon 1 、ε 2 A first error level value and a second error level value, delta, respectively 1 Kp is the first error variation level value 0 、Ki 0 、Kd 0 The primary of the proportional, integral and differential adjustment coefficients, respectivelyA value; kp 01 、Kp 02 、Kp 03 Respectively a preset first proportion regulating value, a preset second proportion regulating value and a preset third proportion regulating value; ki (Ki) 01 、Ki 02 、Ki 03 The first integral regulating value, the second integral regulating value and the third integral regulating value are respectively preset; kd 01 、Kd 02 、Kd 03 、Kd 04 The differential control device comprises a first differential control value, a second differential control value, a third differential control value and a fourth differential control value which are preset respectively; and Kp 01 ≈Kp 03 ＞Kp 02 ,Kd 01 ≈Kd 02 ≈Kd 03 ＜Kd 04 ,0＝Ki 01 ＜Ki 02 ＜Ki 03 ,ε 1 ＞ε 2 ＞0,δ 1 > 0. The symbol "≡" in the present invention means that the error of the two numbers is within 5% of the average value of the two numbers.
Taking the control of the azimuth angle of the turntable as an example, the following values can be adopted:
ε 1 ＝0.3，ε 2 ＝0.1，δ 1 ＝1；Kp 01 ＝Kp 03 ＝30，Kp 02 ＝15；
Ki 01 ＝0，Ki 02 ＝1，Ki 03 ＝10；
Kd 01 ＝12，Kd 02 ＝12，Kd 03 ＝12，Kd 04 ＝18。
preferably, the PID control unit 500 calculates the PID parameter value by the following formula:
Kp＝Kp 0 +ΔKp
Figure BDA0002609986790000081
wherein Kp, ki and Kd are PID parameter values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively; Δkp, Δki, and Δkd are the amounts of change in the proportional adjustment coefficient, the integral adjustment coefficient, and the differential adjustment coefficient, respectively.
The PID control unit 500 calculates the control output by the following formula:
Figure BDA0002609986790000082
where u (k) is the control output of the kth time, e (k) is the error value calculated by the kth time, and e (j) is the error value calculated by the jth time; e (k-1) is the error value of the k-1 th calculation.
The invention realizes double setting of PID parameters through the expert control unit, the fuzzy control unit and the PID control unit.
Referring to FIG. 3, a flow chart of a PID control method according to a preferred embodiment of the invention is shown. As shown in fig. 3, the PID control method provided in this embodiment includes the following steps:
s1, calculating an error value according to an input controlled quantity r and an output feedback quantity y control output quantity;
s2, calculating an error change rate ec according to the error value e;
s3, performing self-adaptive setting on PID parameters of the PID controller according to the error value e and the error change rate by using a fuzzy rule, and outputting the change quantity of the PID parameters; preferably, the PID parameters include: proportional, integral and differential adjustment coefficients. For example, the error value e and the relevant characteristic quantity of the error value are used as input signals, and the variable quantity of a group of PID parameters, namely the variable quantity delta Kp of the proportional adjustment coefficient, the variable quantity delta Ki of the integral adjustment coefficient and the variable quantity delta Kd of the differential adjustment coefficient, are obtained through the processes of blurring, fuzzy reasoning and defuzzification.
S4, obtaining an initial value of the PID parameter according to the error value and the error change rate by using an expert knowledge base;
preferably, in the step S4, initial values of the corresponding PID parameters are generated according to the error value and the error rate according to the following rule mathematical model:
|e|≥ε 1 when Kp 0 ＝Kp 01 ,Ki 0 ＝Ki 01 ,Kd 0 ＝Kd 01 ；
ε 2 ≤|e|＜ε 1 When Kp 0 ＝Kp 02 ,Ki 0 ＝Ki 02 ,Kd 0 ＝Kd 02 ；
|e|＜ε 2 And |ec| is not less than delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 03 ；
|e|＜ε 2 And |ec| < delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 04
Where e is the calculated error value and ec is the calculated error rate; epsilon 1 、ε 2 A first error level value and a second error level value, delta, respectively 1 Kp is the first error variation level value 0 、Ki 0 、Kd 0 Initial values of a proportional adjustment coefficient Kp, an integral adjustment coefficient Ki and a differential adjustment coefficient Kd are respectively set; kp 01 、Kp 02 、Kp 03 Respectively a preset first proportion regulating value, a preset second proportion regulating value and a preset third proportion regulating value; ki (Ki) 01 、Ki 02 、Ki 03 The first integral regulating value, the second integral regulating value and the third integral regulating value are respectively preset; kd 01 、Kd 02 、Kd 03 、Kd 04 The differential control device comprises a first differential control value, a second differential control value, a third differential control value and a fourth differential control value which are preset respectively; and, in addition, the processing unit,
Kp 01 ≈Kp 03 ＞Kp 02 ,Kd 01 ≈Kd 02 ≈Kd 03 ＜Kd 04 ,0＝Ki 01 ＜Ki 02 ＜Ki 03 ,ε 1 ＞ε 2 ＞0,δ 1 ＞0。
s5, obtaining PID parameter values according to the initial values and the variation of the PID parameters during each PID calculation, and calculating control output quantity to the controlled object according to the PID parameter values.
Preferably, the PID parameter values are calculated in this step S5 by the following formula:
Kp＝Kp 0 +ΔKp
Figure BDA0002609986790000101
wherein Kp, ki and Kd are PID parameter values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively; Δkp, Δki, and Δkd are the amounts of change in the proportional adjustment coefficient, the integral adjustment coefficient, and the differential adjustment coefficient, respectively.
Preferably, the control output is calculated in this step S5 by the following formula:
Figure BDA0002609986790000102
where u (k) is the control output of the kth time, e (k) is the error value calculated by the kth time, and e (j) is the error value calculated by the jth time; e (k-1) is the error value of the k-1 th calculation.
The above-described PID control method can be implemented using the PID controller of fig. 1.
Please refer to fig. 4 for a graph of error mean square error trend for four control methods of adding sinusoidal disturbance, and fig. 5 for a graph of error mean square error trend for four control methods of adding step disturbance. In the experiment, tracking error mean square error of four control modes of PID control, expert PID control, fuzzy PID control and expert-fuzzy PID control are obtained by tracking a sinusoidal curve with a period of 2s and an amplitude of 10 degrees and respectively applying a sinusoidal interference signal and a step interference signal to the system, wherein the expert-fuzzy PID control is the PID controller of the invention. Therefore, the expert-fuzzy PID control error mean square error is smaller, and the control performance is more excellent.
In summary, an expert control system is introduced into the invention, an expert knowledge base is constructed through a certain experience rule, error values e in different ranges and error related characteristic quantities correspond to different sets of PID parameters, when the controller works, a corresponding set of Kp, ki and Kd values are obtained through judging the input error values e and the error related characteristic quantities, the set of values are used as initial values of fuzzy PID control, the initial values are switched according to the change of errors under the method, so that the PID controller realizes double setting of expert control and fuzzy control, and through simulation experiments, the expert-fuzzy PID controller has obvious advantages in system stability and error control compared with the common PID controller, the expert PID controller and the fuzzy PID controller.
It should be noted that the PID control method of the present invention is the same as the PID controller in principle and inventive concept, so that the detailed description of the PID controller embodiment is also applicable to the PID control method.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A PID controller, comprising:
an error calculation unit for calculating an error value based on the input controlled amount and the output feedback amount;
an error change rate calculation unit for calculating an error change rate from the error value;
the fuzzy control unit is connected with the error calculation unit and the error change rate calculation unit and is used for receiving the error value and the error change rate, adaptively setting the PID parameters of the PID controller by utilizing a fuzzy rule and outputting the change quantity of the PID parameters;
the expert control unit is connected with the error calculation unit and the error change rate calculation unit and is used for receiving the error value and the error change rate and obtaining an initial value of the PID parameter by utilizing an expert knowledge base;
the PID control unit is connected with the fuzzy control unit, the expert control unit, the error calculation unit and the error change rate calculation unit, and is used for obtaining a PID parameter value according to the initial value and the change amount of the PID parameter during each PID calculation and calculating a control output quantity to a controlled object according to the PID parameter value;
the expert control unit generates initial values of the corresponding PID parameters according to the error value and the error change rate according to the following rule mathematical model:
|e|≥ε 1 when Kp 0 ＝Kp 01 ,Ki 0 ＝Ki 01 ,Kd 0 ＝Kd 01 ；
ε 2 ≤|e|＜ε 1 When Kp 0 ＝Kp 02 ,Ki 0 ＝Ki 02 ,Kd 0 ＝Kd 02 ；
|e|＜ε 2 And |ec| is not less than delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 03 ；
|e|＜ε 2 And |ec| < delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 04
Where e is the calculated error value and ec is the calculated error rate; epsilon 1 、ε 2 A first error level value and a second error level value, delta, respectively 1 Kp is the first error variation level value 0 、Ki 0 、Kd 0 Initial values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient are respectively; kp 01 、Kp 02 、Kp 03 Respectively a preset first proportion regulating value, a preset second proportion regulating value and a preset third proportion regulating value; ki (Ki) 01 、Ki 02 、Ki 03 The first integral regulating value, the second integral regulating value and the third integral regulating value are respectively preset; kd 01 、Kd 02 、Kd 03 、Kd 04 The differential control device comprises a first differential control value, a second differential control value, a third differential control value and a fourth differential control value which are preset respectively; and Kp 01 ≈Kp 03 ＞Kp 02 ,Kd 01 ≈Kd 02 ≈Kd 03 ＜Kd 04 ,0＝Ki 01 ＜Ki 02 ＜Ki 03 ,ε 1 ＞ε 2 ＞0,δ 1 ＞0。
2. The PID controller of claim 1, wherein the PID parameters comprise: proportional, integral and differential adjustment coefficients.
3. PID controller according to claim 1, characterized in that the PID control unit calculates the PID parameter value by the following formula:
Figure FDA0004226544270000021
wherein Kp, ki and Kd are PID parameter values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively; Δkp, Δki, and Δkd are the amounts of change in the proportional adjustment coefficient, the integral adjustment coefficient, and the differential adjustment coefficient, respectively.
4. A PID controller according to claim 3, characterized in that the PID control unit calculates the control output by the following formula:
Figure FDA0004226544270000022
where u (k) is the control output of the kth time, e (k) is the error value calculated by the kth time, and e (j) is the error value calculated by the jth time; e (k-1) is the error value of the k-1 th calculation.
5. A PID control method, characterized by comprising the steps of:
calculating an error value according to the input controlled quantity and the output feedback quantity;
calculating an error change rate according to the error value;
performing self-adaptive setting on PID parameters of the PID controller according to the error value and the error change rate by using a fuzzy rule, and outputting the change quantity of the PID parameters;
obtaining an initial value of the PID parameter according to the error value and the error change rate by using an expert knowledge base;
obtaining PID parameter values according to the initial values and the variation of the PID parameters during each PID calculation, and calculating control output quantity to a controlled object according to the PID parameter values;
in the method, a mathematical model generates initial values of corresponding PID parameters according to error values and error change rates according to the following rules:
|e|≥ε 1 when Kp 0 ＝Kp 01 ,Ki 0 ＝Ki 01 ,Kd 0 ＝Kd 01 ；
ε 2 ≤|e|＜ε 1 When Kp 0 ＝Kp 02 ,Ki 0 ＝Ki 02 ,Kd 0 ＝Kd 02 ；
|e|＜ε 2 And |ec| is not less than delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 03 ；
|e|＜ε 2 And |ec| < delta 1 When Kp 0 ＝Kp 03 ,Ki 0 ＝Ki 03 ,Kd 0 ＝Kd 04
Where e is the calculated error value and ec is the calculated error rate; epsilon 1 、ε 2 A first error level value and a second error level value, delta, respectively 1 Kp is the first error variation level value 0 、Ki 0 、Kd 0 Initial values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient are respectively; kp 01 、Kp 02 、Kp 03 Respectively a preset first proportion regulating value, a preset second proportion regulating value and a preset third proportion regulating value; ki (Ki) 01 、Ki 02 、Ki 03 The first integral regulating value, the second integral regulating value and the third integral regulating value are respectively preset; kd 01 、Kd 02 、Kd 03 、Kd 04 The differential control device comprises a first differential control value, a second differential control value, a third differential control value and a fourth differential control value which are preset respectively; and Kp 01 ≈Kp 03 ＞Kp 02 ,Kd 01 ≈Kd 02 ≈Kd 03 ＜Kd 04 ,0＝Ki 01 ＜Ki 02 ＜Ki 03 ,ε 1 ＞ε 2 ＞0,δ 1 ＞0。
6. The PID control method of claim 5, wherein the PID parameters comprise: proportional, integral and differential adjustment coefficients.
7. The PID control method according to claim 5, wherein the PID parameter values are calculated by the following formula:
Figure FDA0004226544270000031
wherein Kp, ki and Kd are PID parameter values of the proportional adjustment coefficient, the integral adjustment coefficient and the differential adjustment coefficient respectively; Δkp, Δki, and Δkd are the amounts of change in the proportional adjustment coefficient, the integral adjustment coefficient, and the differential adjustment coefficient, respectively.
8. The PID control method according to claim 7, characterized in that the control output is calculated by the following formula:
Figure FDA0004226544270000032
where u (k) is the control output of the kth time, e (k) is the error value calculated by the kth time, and e (j) is the error value calculated by the jth time; e (k-1) is the error value of the k-1 th calculation.
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