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| 引用本文: | 禹威威, 申远, 徐小丽, 王园园.基于PSO的PID参数整定仿真与研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2020，37（4）：14-19 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 基于PSO的PID参数整定仿真与研究 |
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禹威威, 申远, 徐小丽, 王园园
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合肥师范学院 电子信息系统仿真设计安徽省重点实验室, 安徽 合肥 230601
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| 摘要: |
| 针对工业控制过程中经验PID整定耗时耗力、精度低且稳定性能差等问题进行研究，提出采用标准粒子群算法可实现对 PID控制器参数的快速优化且收敛效果明显；通过重点分析PSO算法中的不同惯性权重以及学习因子分别对被控对象系统控制优化性能的影响，深入研究算法参数各部分的作用及其设置范围，使基于PSO算法的PID整定方法能够获得最优的控制效果及更广阔的应用前景；最后，应用Matlab软件平台，并结合Simulink系统进行算例数字仿真分析：通过对比不同惯性权重及学习因子情况下的仿真结果，证明方法的鲁棒性强；通过对比传统Z-N方法和遗传算法整定，证明了方法的优越性。 |
| 关键词: PID控制 标准粒子群算法 参数整定 惯性权重 学习因子 |
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| Simulation and Research of PID Controller Parameter Tuning Based on PSO |
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YU Wei-wei, SHEN Yuan, XU Xiao-i, WANG Yuan-yuan
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Anhui Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Anhui Hefei 230601, China
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| Abstract: |
| According to the problem in the consumption of time and power, low precision and poor stability for experiential PID tuning in the process of industrial control,this paper proposes to use standard particle swarm optimization (PSO) to realize the rapid optimization of PID controller parameters and obvious effect on convergence. By focusing on the analysis of the influence of different inertia weights and learning factor in PSO algorithm on the control optimization performance of the controlled object system, the function of each part of the algorithm parameters and their setting range are further studied, so that PID tuning method based on PSO algorithm can obtain the optimal control effect and broader application prospect. Finally, by using Matlab software platform and by combining Simulink system to conduct example digital simulation analysis, it is proved that this method is strongly robust based on the comparison of the simulated results of different inertia weights and learning factor and that this method is superior by the comparison of traditional Z N method and genetic algorithm tuning. |
| Key words: PID control standard particle swarm optimization algorithm parameter tuning inertia weight learning factor |
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