永磁同步电机RBF-PID控制系统建模与仿真  

Research on Modeling and Simulation of RBF-PID Control System for Permanent Magnet Synchronous Motor

作　　者：李沛隆 黄勇 孙先波 易金桥 梁树先 叶建聪 LI Peilong;HUANG Yong;SUN Xianbo;YI Jinqiao;LIANG Shuxian;YE Jiancong(School of Information Engineering,Hubei Minzu University,Enshi 445000,China;Guangzhou Fengbiao Education Technology Company Limited,Guangzhou 510700,China)

机构地区：[1]湖北民族大学信息工程学院,湖北恩施445000 [2]广州风标教育技术股份有限公司,广州510700

出　　处：《湖北民族大学学报（自然科学版）》2021年第4期462-466,475,共6页Journal of Hubei Minzu University:Natural Science Edition

基　　金：教育部产学合作协同育人项目(201802149044).

摘　　要：对于永磁同步电机的矢量控制,传统的控制算法存在参数自适应整定较难,转速到达平稳时间较长、转矩波动较大、转速及转矩控制精度不高等问题.为此,将RBF神经网络融合到PID控制器中,依靠前者较快的学习收敛速度来改进参数整定,设计出RBF-PID控制器的模型.Matlab/Simulink仿真结果表明:设计的RBF-PID系统相较于传统PID控制在电机启动后到达稳态的时间提高了81.92%,受到干扰后恢复速度提高了10.87%,运行更稳定,为永磁同步电机控制系统提供了有效的控制策略.For the vector control of permanent magnet synchronous motor(PMSM),the traditional control algorithm has some problems,such as difficult parameter adaptive setting,long time for speed to reach a steady state,large torque fluctuation and low precision of speed and torque control.Therefore,in this paper,the radial basis function(RBF)neural network was integrated into the PID controller,and the parameter setting was improved by the faster learning convergence rate of the former,and the model of RBF-PID controller was designed.The Matlab/Simulink simulation results show that the designed RBF-PID system has increased 81.92%of the time to reach the steady state after the motor is started,and 10.87%of the recovery speed after interference,and the operation is more stable compared with the traditional PID control,which provides an effective control strategy for control system of permanent magnet synchronous motor.

关 键 词：RBF神经网络 PID控制 永磁同步电机 收敛速度 MATLAB/SIMULINK 

分 类 号：TM921[电气工程—电力电子与电力传动]