›› 2016, Vol. 29 ›› Issue (1): 161-.

• 论文 • 上一篇    下一篇

基于RBF神经网络的永磁同步伺服电机控制系统

朱卫云,付东翔,葛懂林   

  1. (上海理工大学 光电信息与计算机工程学院,上海 200093)
  • 出版日期:2016-01-15 发布日期:2016-02-25
  • 作者简介:朱卫云(1987—),女,硕士研究生。研究方向:嵌入式系统,伺服驱动系统,工业机器人。付东翔(1971—),男,副教授,硕士生导师。研究方向:嵌入式系统,伺服驱动系统。

PMSM Control System Based on RBF Neural Network

ZHU Weiyun,FU Dongxiang,GE Donglin   

  1. (School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
  • Online:2016-01-15 Published:2016-02-25

摘要:

针对永磁同步电机控制系统,建立其磁场定向控制数学模型。运用增量式数字PID的方法实现对PMSM的传统PID控制策略。在此基础上,借助RBF神经网络的学习能力,进行PID控制器参数的自适应整定,进一步改善PID控制器的性能。同时,为提高RBF网络性能,采用粒子群算法对网络进行优化。仿真表明,与传统PID控制比较,基于RBF的PID控制系统能提高PID控制器的性能,改善了PMSM控制系统的收敛速度和跟踪精度。

关键词: PMSM, FOC, PID控制器, RBF网络, PSO算法

Abstract:

This paper first proposes the establishment of PMSM mathematical model.Then,the conventional PID control is discussed to achieve PMSM control system by using an incremental PID.The learning ability of RBF neural network offers adaptive PID controller parameter to improve the performance of PID controllers.The particle swarm optimization (PSO) is also proposed to improve the performance of RBF network in.The simulation results indicate that the mode control system based on RBF neural network can improve the performance of PID controller compared with conventional PID control with higher convergence speed and tracking accuracy of PMSM control system.

Key words: PMSM;FOC;PID;RBF neural networks;PSO algorithm

中图分类号: 

  • TM351