摘要
在电机控制中,PI参数调节直接影响电机到达目标转速的效率。针对PI参数调节问题,提出一种将信息熵与混沌粒子群算法相结合的融合算法,并将该算法应用于PI参数优化中。采用信息熵对粒子的多样性进行评估并决定粒子是否变异,弥补了PSO算法容易陷入局部最优和CPSO算法因变异产生粒子偏移的缺点。使用MATLAB对电机控制系统进行仿真,结果显示,改进CPSO算法优化的参数在超调量和稳态时间方面都优于相同电机转速下采用传统PSO算法、Z-N整定方法以及FPA算法优化的参数,表明该算法可很好地应用于电机控制的PI参数优化,且性能指标优良。
PI control is an essential approach for controlling speed from a Brushless DC(BLDC) motor to achieve stable operation. This paper presents an improved algorithm based on chaos particle swarm optimization(CPSO)and combined with information entropy to perform an optimal PI tuning for speed control of BLDC motor. The algorithm uses information entropy to evaluate the diversity of particles and determine whether particles mutate or not. The problems on local option from PSO and particle shift from CPSO can be removed by the improved algorithm. The simulation by MATLAB indicated the overshoot and settle time are better than the previous PSO,Z-N,and FPA Algorithm. Our results demonstrated that the improved algorithm can be suitable for PI controller with optimal parameters.
作者
顾亚明
GU Ya-ming(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《软件导刊》
2022年第10期131-135,共5页
Software Guide
基金
上海市自然科学基金项目(20ZR1437900)。