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

Abstract

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

CLC Number:

TP273

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

Figures/Tables 7

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Table 1

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References 16

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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

Particle Swarm Optimization Based PID Controller Parameter Optimization

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