基于鲸鱼算法的开关磁阻电机PID参数整定

doi:10.16180/j.cnki.issn1007-7820.2025.04.009

摘要/Abstract

摘要：

针对开关磁阻电机PID(Proportional Integral Derivative)控制中存在的稳定性差、参数调节困难等问题,文中基于传统开关磁阻电机PID调速系统引入鲸鱼优化算法,将改进时间绝对误差函数作为适应度函数对K_p、K_i、K_d这3个控制参数进行整定。在MATLAB/Simulink仿真平台搭建了三相6/4极开关磁阻电机的PID参数整定系统,分析了传统经验PID调参和算法整定参数的效果对比,并将鲸鱼算法的优化效果与粒子群算法、遗传算法和灰狼优化算法结果进行对比。仿真结果表明,所提方法获得的PID参数较精确,其效果优于3种对比算法。相比于经验法整定参数,鲸鱼算法整定参数响应速度提升了51.10%,误差减小了0.67%,使调速系统具有更快、更稳定的响应特性。

关键词: 开关磁阻电机, 双闭环系统, PID控制, 改进ITAE函数, 鲸鱼算法, Simulink, 参数整定, 响应特性

Abstract:

In view of the problems of poor stability and difficult parameter adjustment in switched reluctance motor PID(Proportional Integral Derivative) controller, the whale optimization algorithm is introduced based on traditional switched reluctance motor controller, and improved integral time absolute error function is used as fitness function to adjust three control parameters of K_p, K_i and K_d.A parameter tuning system of three-phase 6/4-pole switched reluctance motor is built on MATLAB/Simulink simulation platform. The effect of traditional empirical parameter tuning algorithm is analyzed and compared with the results of particle swarm optimization, genetic algorithm and gray wolf optimization. The simulation results show that the PID parameters obtained by the proposed method are more accurate and its effect is better than the three comparison algorithms.Compared with the empirical method, the response speed of the whale algorithm is increased by 51.10%, and the error is reduced by 0.67%, which makes the speed control system have faster and more stable response characteristics.

Key words: switched reluctance motor, double closed loop system, PID control, improved ITAE function, whale optimization algorithm, Simulink, parameters tuning, response characteristic

中图分类号:

TP273

韦润通, 王智冲, 李学琪. 基于鲸鱼算法的开关磁阻电机PID参数整定[J]. 电子科技, 2025, 38(4): 59-65.

WEI Runtong, WANG Zhichong, LI Xueqi. Research on PID Parameter Tuning of Switch Reluctance Motor Based on Whale Optimization Algorithm[J]. Electronic Science and Technology, 2025, 38(4): 59-65.

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优化算法	K_p	K_i	K_d	适应度
经验法	10.000 0	1.000 0	1.000 0	30.990
PSO	12.416 0	0.231 5	0.042 4	9.190
GA	49.263 1	0.297 3	0.431 2	9.208
GWO	45.355 3	0.257 2	0.214 9	9.151
WOA	49.768 6	0.245 3	0.136 1	9.141