基于改进粒子群算法的无刷直流电机控制研究

Brushless DC Motor Control Research Based on Improved Particle Swarm Optimization Algorithm

文章针对无刷直流电机(BLDC)复杂的、耦合的非线性的特点,克服传统的控制算法控制速度精度不高、响应速度慢等缺点,提出了一种基于改进的粒子群算法(PSO)对PID控制器系数自整定的无刷直流电机控制新策略。在Matlab/Simulink中搭建BLDC控制系统的仿真模型,并实现双闭环的控制:速度环采用改进粒子群算法优化PID控制,电流环采用滞环电流控制。仿真结果验证了该控制方法对无刷直流电机调速系统具有良好的快速性、稳定性和鲁棒性。

To consider the complex and coupled nonlinear characteristics of a brushless DC motor, and to overcome the low speed accuracy and slow response shortcoming of the traditional control algorithm, a new strategy for the coefficient of PID controller tuning of brushless DC motor control system simulation modeling based on improved particle swarm optimization （PSO） is proposed in this paper. Building a simulation model of BLDC control system in Matlab/Simulink, and to achieve the dual closed-loop con- trol： The velocity loop adopts the PID control based on particle swarm optimization, the current loop uses hysteresis current control. Simulation results verify the control method of brushless DC motor speed con- trol system has more rapidity, stability and robustness.