Continuous stirring reaction kettle temperature control method for optimizing PID based on improved sparrow search algorithm

Abstract

The invention belongs to the field of process equipment control, and provides a PID (Proportion Integration Differentiation) controller optimization method (BCGSSA-PID) based on an improved sparrow search algorithm in order to solve the problem that the system temperature is difficult to control due to nonlinearity, time lag and the like in the production process of a typical thermotechnical object continuous stirred tank reactor (CSTR). According to the method, firstly, three parameters of a PID controller are optimized through a BCGSSA algorithm, an optimal parameter combination of a proportionality coefficient Kp, an integral coefficient Ki and a differential coefficient Kd is found, and then the optimal parameter combination is used for temperature control of a CSTR system. Compared with a traditional PID control method, the method can automatically select a better parameter combination, and avoids the problems that time and labor are consumed and an ideal control effect cannot be achieved when parameters are selected manually depending on experience. According to the method, the high response speed is guaranteed, the overshoot suppression is higher, the system can reach the steady state more quickly, and the dynamic performance of the system is remarkably improved. Meanwhile, the method has high robustness and adaptivity, and the system temperature can be ideally controlled even under the condition that disturbance is introduced into the system and a system model is mismatched.

Description

Technical Field

[0001] The present invention relates to the field of process equipment control, and in particular to a temperature control method for optimizing PID using a swarm intelligent algorithm. Background Art

[0002] As a common chemical reactor, the continuous stirred tank reactor (CSTR) has the characteristics of multivariable, time lag and nonlinearity, which makes its parameter identification difficult and control difficult. The application of various advanced control technologies such as predictive control, sliding film control, fuzzy control and other control strategies has received close attention. The integration of various advanced control algorithms to improve the control effect of CSTR system is the frontier direction of current research. Although various new control algorithms continue to emerge with the development of control theory, PID control has good effect and strong robustness. It is still widely used in the industrial field. Therefore, in-depth research on PID control is still necessary. The performance of traditional PID is highly dependent on the setting of three parameters: proportional, integral and differential. Its parameter adjustment method often depends on the experience and trial and error of engineers. The process is cumbersome and time-consuming, and may not meet the requirements of temperature control accuracy and stability of CSTR system. The sparrow search algorithm with strong optimization ability, fast convergence speed and high convergence accuracy is used to optimize the PID controller to improve the control effect. Summary of the invention

[0003] The present invention aims to solve the difficult problem in the temperature control of a continuous stirred reactor, and proposes a temperature control method for a continuous stirred reactor based on an improved sparrow search algorithm to optimize PID. Specifically, the technical solution of the present invention is as follows: an improved sparrow search algorithm (BCGSSA) is used to optimize the proportional coefficient, integral coefficient, and differential coefficient of the PID controller to find the optimal combination of three parameters to ensure better control of the continuous stirred reactor system. At the same time, this method also has strong robustness and adaptability, and can ideally control the system temperature even when interference is introduced into the system and the system model is mismatched. Specifically, the specific steps of the present invention are as follows: S1: Improvement of the sparrow search algorithm; by improving initialization, the finder update formula and the follower update formula, the algorithm's search capability, convergence speed and convergence accuracy are improved; S2: Use the improved sparrow search algorithm to optimize the three parameters of the PID controller; optimize the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd; S3: Robustness and adaptability testing of continuous stirred tank reactor systems; S1 specifically includes: S11: Improved population initialization: Use TENT chaotic mapping to initialize the population; S12: Improvement of the discoverer update formula: using the butterfly algorithm global search strategy; S13: Follower update formula improvement: using Cauchy and Gaussian mutation fusion strategy; S2 is used to find the optimal parameter combination of the proportional coefficient Kp, the integral coefficient Ki and the differential coefficient Kd. S2 specifically includes: S21: Use the error performance index function with a time lag factor as the fitness function to initialize the optimal fitness value; S22: Establish the initial settings of the sparrow search algorithm: the population size is set to 30; the number of iterations is set to 50; the dimension is set to 3; the range of the three parameters to be optimized is [0,20]; S23: For each sparrow in the population (combination of Kp, Ki and Kd), calculate its fitness value; S24: Update the next generation fitness value according to the initialized optimal fitness value. If the next generation fitness value is higher than the previous generation fitness value, substitute it as the current optimal value position; S25: compare its fitness value with the global optimal value. If the fitness value is better, replace it again until the end condition is reached; S26: The iteration stops and Kp, Ki and Kd corresponding to the optimal sparrow position are output; S3 specifically includes: S31: Given a step signal as an input signal of the continuous stirred reactor, and measuring its output performance curve; S32: Add an interference signal with an amplitude of 0.1 and a duration of 5 seconds to the input end to measure its anti-interference ability; S33: Changing the transfer function of the continuous stirred tank reactor system in the simulation model to determine its adaptive capability; The present invention is a temperature control method designed for the hysteresis and nonlinear characteristics of the continuous stirred tank production process, and the PID controller is optimized by applying several advantages of the improved sparrow search algorithm. Compared with the traditional PID control method, the method can automatically select a better parameter combination, avoiding the problem that the manual selection of parameters based on experience is time-consuming and labor-intensive and fails to achieve the ideal control effect. The method not only ensures a faster response speed, but also has a stronger suppression of overshoot, which can enable the system to reach a steady state faster and significantly improve the dynamic performance of the system. At the same time, it also has strong robustness and adaptability, and can ideally control the system temperature even when disturbances are introduced into the system and the system model is mismatched. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 This is a flow chart of the temperature control method of a continuous stirred reactor based on the improved sparrow search algorithm to optimize PID; Figure 2This is a model diagram of the temperature control method of a continuous stirred reactor based on the improved sparrow search algorithm to optimize PID; Figure 3 The step response curves of the three algorithms of traditional PID, SSA-PID and BCGSSA-PID for the control of continuous stirred reactor are shown; Figure 4 This is a comparison chart of the three algorithms after adding an interference signal with an amplitude of 0.1 and a duration of 5 seconds at the input end at 200 seconds; Figure 5 This is a comparison of the step response curves of the SSA-PID and BCGSSA-PID algorithms for continuous stirred reactor control after the system model mismatch. DETAILED DESCRIPTION

[0005] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementations. Figure 1 As shown, this embodiment provides a continuous stirred reactor temperature control method based on an improved sparrow search algorithm to optimize PID, comprising the following steps: S1: Improvement of sparrow search algorithm; The analysis found that the sparrow search algorithm still has certain defects in convergence speed, global search ability, local search ability, convergence speed and convergence accuracy. The algorithm's optimization ability is improved by improving the update formula of the original algorithm. Firstly, the TENT chaotic mapping is introduced in the initialization stage to uniformly initialize the population to improve the quality of the initial solution. Then, the butterfly algorithm global search strategy is introduced in the discoverer position update formula to enhance the discoverer's leading role in the population and strengthen the algorithm's global search ability. Finally, the Cauchy and Gaussian mutation strategies are used alternately in the follower position update formula to ensure that it can accurately locate the optimal solution or the area close to the optimal solution, thereby improving the algorithm's optimization accuracy. S2: Use the sparrow search algorithm to optimize the PID controller and find the optimal parameter combination Kp, Ki and Kd; S2 specific steps: S21: Use the error performance index function with a time lag factor as the fitness function to initialize the optimal fitness value; S22: Establish the initial settings of the sparrow search algorithm: the population size is set to 30; the number of iterations is set to 50; the dimension is set to 3; the range of the three parameters to be optimized is [0,20]; S23: For each sparrow in the population (combination of Kp, Ki and Kd), calculate its fitness value; S24: Update the next generation fitness value according to the initialized optimal fitness value. If the next generation fitness value is higher than the previous generation fitness value, substitute it as the current optimal value position; S25: compare its fitness value with the global optimal value. If the fitness value is better, replace it again until the end condition is reached; S26: The iteration stops and Kp, Ki and Kd corresponding to the optimal sparrow position are output; S3: Simulation test of temperature system of continuous stirred reactor; S3 specifically includes: S31: Given a step signal as an input signal of the continuous stirred reactor, and measuring its output performance curve; S32: Add an interference signal with an amplitude of 0.1 and a duration of 5 seconds to the input end to measure its anti-interference ability; S33: Changing the transfer function of the continuous stirred tank reactor system in the simulation model to determine its adaptive capability; S3 specific steps: S31: Solve the mathematical model of the continuous stirred reactor according to the mechanism modeling method, and perform Laplace transformation on it to convert it into the form of transfer function; S32: Establish the corresponding control system model in MATLAB / SIMULINK; Figure 2 This is a simulation model diagram of the temperature control method of the continuous stirred reactor based on the improved sparrow search algorithm to optimize PID; S33: Given a step signal as an input signal of the continuous stirred reactor system, and determining its output performance curve; Figure 3 The step response curves of the three algorithms of traditional PID, SSA-PID and BCGSSA-PID for the control of continuous stirred reactor are shown; S34: At 200 seconds, add an interference signal with an amplitude of 0.1 and a duration of 5 seconds to the input end to measure the anti-interference ability of the system; Figure 4 This is a comparison chart of the three algorithms after adding an interference signal with an amplitude of 0.1 and a duration of 5 seconds at the input end at 200 seconds; S35: Changing the transfer function of the continuous stirred tank reactor system in the simulation model to determine the adaptive ability of the algorithm; Figure 5 This is a comparison of the step response curves of the SSA-PID and BCGSSA-PID algorithms for the continuous stirred reactor control after the system model mismatch; The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A temperature control method for a continuous stirred reactor based on an improved sparrow search algorithm to optimize PID, characterized in that: The following steps are involved: S1: Improvement of sparrow search algorithm; S2: Optimize the three parameters of the PID controller using the improved sparrow search algorithm; S3: Robustness and adaptability testing of the continuous stirred tank reactor system.

2. The temperature control method of a continuous stirred reactor based on an improved sparrow search algorithm to optimize PID according to claim 1, characterized in that The search capability, convergence speed and convergence accuracy have been improved. S1 specifically includes: S11: Improved population initialization: Use TENT chaotic mapping to initialize the population; S12: Improvement of the discoverer update formula: using the butterfly algorithm global search strategy; S13: Follower update formula improvement: using Cauchy and Gaussian mutation fusion strategy.

3. The temperature control method of a continuous stirred reactor based on an improved sparrow search algorithm to optimize PID according to claim 1, characterized in that Find the optimal parameter combination of proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd. The specific steps of S2 are: S21: Use the error performance index function with a time lag factor as the fitness function to initialize the optimal fitness value; S22: Establish the initial settings of the sparrow search algorithm, including the population size, the number of iterations, the dimension, and the ranges of the three parameters to be optimized; S23: For each sparrow in the population (combination of Kp, Ki and Kd), calculate its fitness value; S24: Update the next generation fitness value according to the initialized optimal fitness value. If the next generation fitness value is higher than the previous generation fitness value, substitute it as the current optimal value position; S25: compare its fitness value with the global optimal value. If the fitness value is better, replace it again until the end condition is reached; S26: The iteration stops and Kp, Ki and Kd corresponding to the optimal sparrow position are output.

4. The method for controlling the temperature of a continuous stirred reactor based on an improved sparrow search algorithm to optimize PID according to claim 1, characterized in that: S3 specifically includes: S31: Given a step signal as an input signal of the continuous stirred reactor, and measuring its output performance curve; S32: Add an interference signal with an amplitude of 0.1 and a duration of 5 seconds to the input end to measure its anti-interference ability; S33: Change the transfer function of the continuous stirred tank reactor system in the simulation model to determine its adaptive ability.

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