摘要
为了提高无刷直流电机控制性能,提出了一种基于改进的全面学习粒子群(CLPSO)算法自整定PID参数的控制方法,实现了无刷直流电机调速系统的自适应控制。该方法利用改进的CLPSO算法全局搜索能力强、收敛速度快及收敛精度高等优点,对无刷直流电机控制系统的PID参数进行寻优。MATLAB/Simulink仿真实验表明,在电机启动和突加负载过程中,该方法控制电机的转速和转矩响应速度快、波动小,比传统PID控制方法具有更好的动静态特性和鲁棒性。
To improve the adaptive control performance of brushless DC motor（BLDC）,a new strategy for the coefficient of PID controller tuning based on improved comprehensive learning particle swarm optimizer（CLPSO）was proposed in this paper.The improved CLPSO is effective and precise,which make it suitable for the coefficient of PID controller tuning.Simulation results in MATLAB/Simulink verify this brushless DC motor speed control system is more rapid,stabilized and robust compared with traditional PID controller.
作者
李梦凡
王云
卫丽超
LI Meng-fan;WANG Yun;WEI Li-chao(Xi＇an Microelectronics Technology Institute,Xi＇an 710054,China)
出处
《机械工程与自动化》
2018年第5期91-93,共3页
Mechanical Engineering & Automation
关键词
无刷直流电机
基于改进的全面学习粒子群算法
自适应控制
仿真
brushless DC motor
improved comprehensive learning particle swarm optimizer
adaptive control
simulation
