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CN-122018285-A - Fuzzy PID initial parameter setting method based on step response model

CN122018285ACN 122018285 ACN122018285 ACN 122018285ACN-122018285-A

Abstract

The invention discloses a fuzzy PID initial parameter setting method based on a step response model, which comprises the following steps of (1) system identification, (2) fuzzy PID initial parameter setting, and (3) fuzzy PID dynamic control, wherein the initial parameter set obtained in the step (2) is dynamically corrected in real time through dynamic correction, and then a control signal adapting to the current working condition is output. The invention can be directly transplanted to common control platforms such as industrial PLC, singlechip and the like, can meet the requirements of various temperature control scenes such as industrial production, precision experiments, biomedical and the like, and has wide application prospect and popularization value.

Inventors

  • ZHANG CHAOFAN
  • ZHANG QIFA
  • DING YUPING

Assignees

  • 黄山奥仪电器有限公司

Dates

Publication Date
20260512
Application Date
20260317

Claims (4)

  1. 1. A fuzzy PID initial parameter setting method based on a step response model is characterized by comprising the following steps: Step (1), system identification, namely applying a step input signal with preset amplitude to a controlled temperature object, synchronously acquiring input step quantity and corresponding temperature response data in real time as initial temperature data through a high-precision temperature sensor and a data acquisition module, performing filtering pretreatment on the acquired initial temperature data to obtain pretreated temperature data after treatment, constructing a parameter estimation model based on a least square method, and iteratively estimating the pretreated temperature data through the parameter estimation model to obtain key dynamic parameters of the system, wherein the key dynamic parameters of the system comprise system gain K reflecting the relation of input and output amplitude, a time constant T representing the response speed of the system and delay time L describing signal transmission delay; Setting fuzzy PID initial parameters, namely taking the system gain K, the time constant T and the delay time L obtained by the system identification in the step (1) as input variables of a PID initial parameter mapping model, carrying out multi-parameter coupling calculation through a preset Mac_PID function, and finally outputting an initial parameter set of a PID controller, wherein the initial parameter set comprises integral time Ti for determining integral control intensity, differential time Td for representing differential control action, a proportional coefficient Mul_Kp for adjusting proportional control weight, an integral coefficient Mul_Ki for influencing integral accumulation rate and a differential coefficient Mul_Kd for managing differential response sensitivity; Step (3), fuzzy PID dynamic control, namely acquiring a set temperature value and an actual temperature feedback value of a temperature control system in real time, calculating deviation of the set temperature value and the actual temperature feedback value to obtain an error E, calculating the change rate of the error E in unit time to obtain an error change rate Ec through a sliding window method, constructing a fuzzy reasoning rule base, taking the error E and the error change rate Ec as input quantities of fuzzy reasoning, carrying out fuzzy reasoning through the fuzzy reasoning rule base to obtain dynamic correction quantity of PID parameters, carrying out real-time dynamic correction on the initial parameter set obtained in the step (2) through the dynamic correction quantity, and then outputting a control signal adapting to the current working condition.
  2. 2. The method for setting a fuzzy PID initial parameter based on a step response model of claim 1, wherein the step (1) comprises the steps of: Step (11), initializing, namely collecting continuous 10 groups of temperature data of a controlled object in a stable initial state through a high-precision temperature sensor and a data acquisition module, taking an average value as initial temperature data, and carrying out comprehensive zero clearing treatment on accumulated data required in the system identification process in the step (1), wherein the accumulated data comprises key intermediate data required for constructing a least square equation set, and the key intermediate data comprises a variable sum term, an output integral accumulated value and a product accumulated term of the variable and the output value; Step (12), real-time data acquisition and updating, wherein the high-precision temperature sensor and the data acquisition module acquire real-time data by setting a fixed sampling frequency of 8 Hz; in the real-time data acquisition process, synchronously recording the amplitude of an input step quantity and the temperature output value at the current moment, calculating the integral accumulation value of the output quantity by adopting a trapezoidal integral method based on the temperature output value at the previous moment and the temperature output value at the current moment, wherein the specific calculation mode of the trapezoidal integral method is that the current integral accumulation value = the previous integral accumulation value+ (the temperature at the current moment + the temperature at the previous moment) multiplied by the sampling period/2; and (13) solving parameters, namely constructing a linear equation set containing three unknown numbers K, T, L based on the coefficient matrix A and the result matrix b obtained in the step (12), introducing a dynamic regularization term into the linear equation set, adaptively adjusting the amplitude of the dynamic regularization term according to the discrete degree of data, solving the linear equation set introduced with dynamic regularization by adopting a Gaussian elimination method, ensuring the convergence of the solution through iteration verification in the solving process, stopping iteration and outputting final system key dynamic parameters, namely a system gain K, a time constant T and a delay time L when the parameter difference value obtained by two adjacent iterations is smaller than a preset threshold value.
  3. 3. The method for setting the initial parameters of the fuzzy PID based on the step response model of claim 1, wherein the Mac_PID function multi-parameter coupling calculation in the step (2) comprises the following steps: Step (21), calculating the influence weights of the delay time L, representing three control parameters Kp, ki and Kd of the PID controller, of the intermediate variable Lamuda: lamuda, and fitting based on a large amount of temperature control experimental data to obtain a calculation formula Lamuda =0.6xL, wherein L is the delay time obtained in the step (1), and the value of the coefficient enables the follow-up PID parameters to adapt to temperature control systems with different delay characteristics; the derivation of the integration time Ti and the differentiation time Td is based on the dynamic characteristic matching principle of the temperature control system, and is derived according to a time constant T, a delay time L and an intermediate variable Lamuda, wherein the calculation formula of Ti is used for balancing steady-state precision and overshoot of the integration control, the calculation formula of Td is used for optimizing the anti-interference capability of the differentiation control, namely Ti=T+ (L x L)/(2 x (Lamuda +L)), td= (L x L (3 x Ti-L))/(6 x Ti (Lamuda +L)), and T is the time constant obtained in the step (1); Calculating core control parameters, namely calculating a proportional coefficient mul_kp, an integral coefficient mul_ki and a differential coefficient mul_kd according to Ti and Td obtained in the step (22) and a system parameter K obtained in the step (1) and a collaborative matching principle of proportional, integral and differential actions, wherein the calculation formulas are mul_kp=Ti/(K (Lamuda +L)); and (24) empirically adjusting the integral coefficient mul_ki, namely empirically adjusting the integral coefficient mul_ki large in the step (23) in a mode of mul_ki=mul_ki 0.5.
  4. 4. The method for setting the initial parameters of the fuzzy PID based on the step response model of claim 1, wherein the step (3) comprises the following sub-steps: Calculating an error and an error change rate, namely calculating the deviation between a set temperature value and an actual temperature feedback value through a comparison module of a temperature control system to obtain an error E, wherein the actual temperature feedback value adopts an average value of 3 groups of continuous temperature feedback values; Step (32), blurring processing: presetting the fuzzy domains of the error E and the error change rate Ec, wherein the fuzzy domain of the error E is set to be [ -5,5], the fuzzy domain of the error change rate Ec is set to be [ -3,3], the continuous error E and the error change rate Ec are respectively converted into corresponding fuzzy quantities through triangle membership functions, the blurring amount includes seven levels of "negative large (NB)" "Negative Medium (NM)" "Negative Small (NS)" "Zero (ZO)" "Positive Small (PS)" "median (PM)" "positive large (PB)"; Step (33) and fuzzy Rule reasoning, namely constructing three fuzzy Rule tables of Kp_rule, ki_rule and Kd_rule based on the step (32), wherein each Rule table comprises 7 multiplied by 7=49 fuzzy rules, for example, when the error E is Positive (PB), the error change rate Ec is Negative (NB), the Kp correction amount is Negative (NB), the Ki correction amount is Zero (ZO) and the Kd correction amount is median (PM), and matching the corresponding fuzzy rules according to the fuzzy amount indexes of the error E and the error change rate Ec by adopting a gravity center method to calculate the accurate correction amount deltaKp, deltaKi, deltaKd of the PID parameter; And (34) outputting parameter correction, wherein the PID initial parameter set is subjected to linear correction based on the accurate correction obtained in the step (33), the correction formulas are Kp=mul_Kp+deltaKp, ki=mul_Ki+deltaKi, kd=mul_Kd+ deltaKd, wherein Kp is a corrected proportional coefficient, ki is a corrected integral coefficient, kd is a corrected differential coefficient, And (3) carrying out upper and lower limit constraint on the parameters after correction, and finally outputting the corrected real-time control parameters to an executing mechanism of the temperature control system.

Description

Fuzzy PID initial parameter setting method based on step response model Technical Field The invention relates to the technical field of temperature control, in particular to a fuzzy PID initial parameter setting method based on a step response model, which is particularly suitable for industrial temperature control scenes with inertial hysteresis and working condition fluctuation, such as constant temperature control in precise electronic element manufacture, temperature regulation and control in biological fermentation process, temperature stability control of a chemical reaction kettle and the like. Background In temperature control scenes such as industrial production, precision experiments and the like, the control precision of temperature parameters directly determines the product quality or the reliability of experimental results, and PID control is widely applied to various temperature control systems due to the characteristics of simple structure, strong robustness and high reliability. However, parameter setting of the traditional PID controller depends on field experience of technicians for a long time, experience difference of different technicians can lead to larger difference of parameter setting results, problems of low setting efficiency, time consumption and labor consumption exist, control precision is poor due to insufficient parameter matching degree, more importantly, once traditional PID parameters are fixed, dynamic characteristic change of a controlled object cannot be self-adapted, for example, in manufacturing of precise electronic elements, when load change is caused by production batch switching or reaction heat change is caused by bacterial group growth in a biological fermentation process, obvious inertia lag and working condition fluctuation can occur in a temperature control system, further control precision is reduced, stability is poor, even problems of excessive overshoot, continuous oscillation and the like occur, and product rejection or experiment failure can be caused when the problems are serious. In order to solve the problems of the traditional PID control, a fuzzy PID control technology is developed, and the self-adaptive capacity of the system is improved to a certain extent by introducing a fuzzy logic rule and dynamically correcting PID parameters according to the running state of the system. The existing fuzzy PID control technology still has obvious defects that firstly, initial parameter setting is not independent of manual experience, a technician needs to preset an initial parameter range according to experience of a similar system and then fine-tune through a trial-and-error method, the process is low in efficiency, actual dynamic characteristics of a current controlled object cannot be fully combined, secondly, the problems of low response speed, large overshoot, large steady-state error and the like of the initial control stage can be caused by mismatching of the initial parameter and the dynamic characteristics of the controlled object, and the requirement of precise temperature control is difficult to meet, thirdly, the prior art lacks a systematic technical scheme for effectively combining the dynamic parameter identification of the controlled object and the initial parameter setting of a fuzzy PID, accurate self-adaptive matching of the initial parameter cannot be realized, the self-adaptive advantage of the fuzzy PID cannot be fully exerted, and further improvement of temperature control performance is limited. Therefore, developing a method capable of realizing accurate self-tuning of fuzzy PID initial parameters based on the actual dynamic characteristics of the controlled object becomes an urgent need in the technical field of current temperature control. Disclosure of Invention The invention aims to provide a fuzzy PID initial parameter setting method based on a step response model, which can effectively solve at least one technical problem existing in the prior art. The invention solves the technical problems by adopting the technical scheme that the fuzzy PID initial parameter setting method based on the step response model comprises the following steps: Step (1), system identification, namely applying a step input signal with preset amplitude to a controlled temperature object, synchronously acquiring input step quantity and corresponding temperature response data in real time as initial temperature data through a high-precision temperature sensor and a data acquisition module, performing filtering pretreatment on the acquired initial temperature data to obtain pretreated temperature data after treatment, constructing a parameter estimation model based on a least square method, and iteratively estimating the pretreated temperature data through the parameter estimation model to obtain key dynamic parameters of the system, wherein the key dynamic parameters of the system comprise system gain K reflecting the relation of input and output amplitud