基于改进型粒子群算法的无刷直流电机速度控制研究

Research on Speed Control of Brushless DC Motor Based on Improved Particle Swarm optimization

无刷直流电机的控制方便,效率突出等特点,依赖于较高的调速性能。传统调速系统所采用的PID调速,日益无法满足工业应用对精度、抗干扰,自适应等调控品质的要求。本文针对以上不足,采用基于改进型粒子群算法的PID调速系统来对电机转速进行调控。鉴于粒子群算法寻优速度不匹配,且易过早陷入局部最优等问题,采用自适应惯性权重法来进行优化改进。通过编写Matlab编程和搭建simulink模型仿真对比,可以看到相对于传统PID控制器,改进后粒子群算法能使控制器有更小的超调量、更快的响应速度和更强的抗干扰能力,显著提高无刷直流电机调速系统性能。

In order to give full play to the characteristics of convenient control and outstanding efficiency,brushless DC motors often require high speed control.The PID speed control used in traditional speed control systems is increasingly unable to meet the requirements of industrial applications for precision,anti-interference,and adaptive control quality.Aiming at the above shortcomings,this paper adopts PID speed regulation system based on improved particle swarm optimization to control the motor speed.In view of the mismatch of the optimization speed of the particle swarm optimization algorithm and the tendency to fall into the local optimum prematurely,the adaptive inertia weight method is used for optimization and improvement.By writing Matlab programming and building a simulink model simulation comparison,you can see that compared with the traditional PID controller,the improved particle swarm algorithm can make the controller have smaller overshoot,faster response speed and stronger anti-interference ability,Significantly improve the performance of the brushless DC motor speed control system.