基于改进粒子群算法的PID参数优化方法研究

孔明1; 张琳2; 张龙龙1

基于改进粒子群算法的PID参数优化方法研究

引用本文：孔明1，张琳2，张龙龙1.基于改进粒子群算法的PID参数优化方法研究[J].计算技术与自动化,2025,(2):38-43

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作者	单位
孔明1，张琳2，张龙龙1	（1.山东新华医疗器械股份有限公司,山东淄博 255000；2.山东新华联合骨科器材股份有限公司,山东淄博 255000)

中文摘要:为解决粒子群优化（PSO）算法探索和利用不平衡导致寻优能力下降问题，提出了一种改进的PSO（IPSO）算法。首先，引入莱维飞行建立种群初始搜索模型提升算法探索能力；其次，建立种群探索行为模型、种群开发行为模型和探索与开发的平衡模型，自适应地平衡寻优过程中算法的探索和利用性能；最后，设计基于IPSO算法的直流电机PID控制器参数优化流程。实验结果表明，相较于对比算法，IPSO算法在CEC 2017函数问题上是最优算法，基于IPSO算法的PID控制器具有更优的控制性能，提出的方法为工程中PID控制器的参数优化提供了参考。

中文关键词:PID PSO算法探索和利用莱维飞行参数优化

Study on PID Parameter Optimization Method Based on Improved Particle Swarm Optimization Algorithm

Abstract:In order to solve the problem of decreasing the optimisation search ability due to the imbalance between exploration and exploitation of particle swarm optimisation (PSO) algorithms, an improved PSO (IPSO) algorithm is proposed. Firstly, Lévy flight is introduced to establish a population initial search model to enhance the algorithm’s exploration ability; secondly, a population exploration behaviour model, a population exploitation behaviour model and an exploration exploitation equilibrium model are established to adaptively balance the algorithm's exploration and exploitation performances in the optimization process; and lastly, a parameter optimization process of the PID controller of a DC motor based on the IPSO algorithm is designed. The experimental results show that the IPSO algorithm is the best algorithm in the CEC 2017 function problem compared to the comparison algorithm, and the PID controller based on the IPSO algorithm has a better control performance, and the proposed method provides a reference for the parameter optimisation of PID controllers in engineering.

keywords:PID PSO algorithm exploration and exploitation Lévy flight parameter optimization

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