基于GA-PSO融合算法的二自由度PID参数优化

doi:10.16180/j.cnki.issn1007-7820.2019.10.011

摘要/Abstract

摘要：

在工业过程控制中,PID参数调节直接影响工业生产的质量和效率。针对PID参数调节难这一问题,文中提出了一种将遗传算法和粒子群算法相结合的智能融合算法,并将该算法应用于二自由度PID参数的优化中。该算法在遗传算法的变异算子中引入粒子群算法,充分发挥两种单一智能算法的优点,并弥补了两者的缺点。算例仿真验证结果显示,该算法可以很好的应用于PID参数优化,且在调节PID参数的过程中具有优良的性能指标数值,在目标值跟踪特性和外扰动抑制特性上具有更好的控制效果。

关键词: 遗传算法, 粒子群算法, 二自由度, 变异算子, 智能算法, 参数优化, 性能指标

Abstract:

In industrial process control systems, the tuning of PID parameters have direct influence on the quality and efficiency of production. Aiming at the difficulty of PID parameter adjustment, an intelligent fusion algorithm combined with genetic algorithm and particle swarm optimization was proposed, and the proposed algorithm was applied to tuning parameters of two-degree-of-freedom PID controller. The algorithm introduced particle swarm optimization algorithm into the mutation operator of genetic algorithm, which fully exploited the advantages of two single intelligent algorithms and made up for the shortcomings of both. The simulation results showed that the proposed algorithm could be applied to tuning PID parameters and excellent performance index values in the process of adjusting the PID parameters. Moreover, it had better control effects on target value tracking characteristics and external disturbance suppression characteristics.

Key words: genetic algorithm, particle swarm optimization, two degrees of freedom, mutation operator, intelligent algorithm, parameter optimization, performance index

中图分类号:

TP273

吴延凯,张伟,马盈满,李佳阳. 基于GA-PSO融合算法的二自由度PID参数优化[J]. 电子科技, 2019, 32(10): 54-59.

WU Yankai,ZHANG Wei,MA Yingman,LI Jiayang. Two Degree of Freedom PID Parameter Optimization Based on GA-PSO Fusion Algorithm[J]. Electronic Science and Technology, 2019, 32(10): 54-59.

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参数	目标值跟踪特性		外扰动抑制特性
参数	K_c₁	T_d₁	K_c₂	T_i₂	T_d₂
GA-PSO	2.083 0	1.000 0	10.000 0	0.996 8	0.503 9
文献[7]	3.292 6	0.703 7	3.850 6	2.275 1	0.571 2
文献[8]	2.851 6	0.847 7	7.187 5	1.025 1	0.625 0
文献[9]	2.204 7	1.000 0	6.630 6	0.873 9	1.000 0

调节方法	目标值跟踪特性			外扰动抑制特性
调节方法	IAE	ISE	t_s	IAE	ISE	t_s
GA-PSO	0.483	0.335	0.790	0.116	0.007	1.320
文献[7]	0.642	0.423	0.750	0.591	0.097	4.070
文献[8]	0.557	0.371	0.760	0.189	0.014	2.140
文献[9]	0.501	0.356	0.780	0.227	0.013	2.510