摘要
为提高控制系统的性能,提出了一种采用改进混沌粒子群(CPSO)算法的PID参数整定方法。该算法将混沌搜索应用到粒子群算法的粒子位置和速度初始化、惯性权重优化、随机常数以及局部最优解邻域点的产生的全过程,使其不仅具有全局寻优能力,而且具有持续与精细的局部搜索能力。3种典型控制系统的PID参数整定实验结果验证了所提方法的有效性,其性能明显优于常规方法。
To improve the performance of control system,a new method for tuning PID parameters was proposed by using modified chaotic particle swarm optimization algorithm（CPSO）.The chaotic search is applied to the initialization of position and velocity of initial swarm,the optimization of inertia weight,the generation of random constant and the generation of the local optimum neighborhood point,so that the algorithm has the ability of global optimization and continuous and precise local search.The experimental results of 3typical control systems show that the proposed method for tuning PID parameters is effective,and the performance is obviously superior to the conventional methods.
出处
《计算机科学》
CSCD
北大核心
2014年第11期278-281,共4页
Computer Science
基金
江苏省基础研究计划(自然科学基金)项目(BK20131124)
徐州工程学院江苏省大型工程装备检测与控制重点建设实验室开放基金项目(JSKLEDC201221)资助
关键词
PID控制
参数整定
混沌优化
粒子群优化算法
PID control
Parameters tuning
Chaotic optimization
Particle swarm optimization algorithm
