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改进微粒群算法优化PID参数的研究
引用本文:刘幸.改进微粒群算法优化PID参数的研究[J].电子设计工程,2011,19(9):79-82.
作者姓名:刘幸
作者单位:空军雷达学院,湖北,武汉,430000
摘    要:微粒群算法是一种新的随机优化算法,算法通过微粒间相互作用发现复杂搜索空间中的最优区域,该算法具有搜索速度快、寻优能力强、算法简单等特点,但也存在普遍的缺点。本文基于微粒群算法容易陷入局部极值和收敛速度慢的缺点,提出一种新的改进算法,介绍了将改进微粒群算法用于PID控制器参数优化的方法,算法实现流程,并结合Matlab强大Simulink系统仿真功能证明了改进算法的有效性,其性能优于经验公式和遗传算法。

关 键 词:微粒群算法  随机优化  PID控制器  参数优化

Improved particle swam optimization and its application in PID parameters optimization
LIU Xing.Improved particle swam optimization and its application in PID parameters optimization[J].Electronic Design Engineering,2011,19(9):79-82.
Authors:LIU Xing
Affiliation:LIU Xing(Air Force Radar College,Wuhan 430000,China)
Abstract:Particle swarm optimization is a new random global optimization algorithm.Through interaction between particles,the algorithm finds the optimal area in complicate searching space.The algorithm feature is simple、ease to implement and powerful function.Meanwhile it has disadvantage so far as its local minimum is concerned and its slow convergence speed.Under this background,the dissertation proposed a new improved algorithm and the improved Particle Swarm Optimization has been used in PID controller to optimize parameters.Combined with power Matlab simulink function,the simulation results verified the effectiveness of Particle Swarm Optimization algorithm and shown that its performance is better than conventional experience method and GA algorithm.
Keywords:particle swarm optimization  stochastic optimization  PID controller  parameter optimization
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