基于粒子群算法的PID控制器参数优化

doi:10.16180/j.cnki.issn1007-7820.2019.06.002

摘要/Abstract

摘要：

粒子群算法是一种智能算法,在PID控制器参数整定的应用中可取得更优的效果。为解决传统的粒子群算法早熟收敛和收敛速度慢的缺点,文中采用了一种基于相似度动态调整惯性权重的方法,即越靠近目前最优粒子的个体被赋予越小的惯性权重值。最后用MATLAB对等温连续搅拌釜反应器仿真。与标准的PSO算法整定方法相比,改进的粒子群算法稳定时间为230.1 s,比传统粒子群算法524.7 s的稳定时间缩小了一半,表明改进的算法对PID控制器的参数优化有着较优的收敛效果。

关键词: 改进PSO算法, PID控制器, 参数整定, 相似度, 惯性权重, 搅拌釜反应器, 仿真

Abstract:

Particle swarm optimization is an intelligent algorithm that can achieve better results in the application of PID controller parameter tuning. In order to solve the shortcomings of the premature convergence and slow convergence of the traditional particle swarm optimization algorithm, this paper adopted a method of dynamically adjusting the inertia weight based on the similarity, that was, the closer to the current optimal particle, the smaller the inertia weight value was assigned. Finally, MATLAB was used to simulate the isothermal continuous stirred tank reactor. Compared with the standard PSO tuning method, the improved particle swarm algorithm had a settling time of 230.1 s, which was half the stability time of the conventional particle swarm optimization algorithm of 524.7 s, indicating that the improved algorithm had better parameters optimization for the PID controller.

Key words: improved PSO algorithm, PID controller, parameter setting, similarity, inertia weight stirred tank reactor, simulation

中图分类号:

TP273

杜美君,张伟,谢亚莲. 基于粒子群算法的PID控制器参数优化[J]. 电子科技, 2019, 32(6): 7-11.

DU Meijun,ZHANG Wei,XIE Yalian. Particle Swarm Optimization Based PID Controller Parameter Optimization[J]. Electronic Science and Technology, 2019, 32(6): 7-11.

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	PSO	改进PSO
平均收敛率	19	31
平均收敛代数	60.332	52.754

		文献[16]	改进PSO
控制参数	K_p	1.726 9	1.479 8
	K_i	0.011 9	0.035 3
	K_d	8.037 1	11.534 6
时间响应	t_r	30.91	29.91
	t_s	524.7	230.1
误差	ISE	48.906 8	47.711 8
	IAE	79.359 3	68.732 0