永磁同步电机神经网络速度控制器设计  

Design of Neural Network Speed Controller for Permanent Magnet Synchronous Motor

作　　者：田雨 康尔良[1] TIAN Yu;KANG Er-liang(School of Electrical and Electronic Engineering,Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]哈尔滨理工大学电气与电子工程学院,哈尔滨150080

出　　处：《微特电机》2020年第6期49-52,共4页Small & Special Electrical Machines

基　　金：黑龙江省科技攻关资助项目(GC04A517)。

摘　　要：针对永磁同步电机非线性、多参数变化以及系统扰动等问题,将传统PID控制与具有强自适应、自学习能力的径向基函数(RBF)神经网络相结合,设计一种永磁同步电机神经网络速度控制器。用RBF神经网络自适应整定PID速度控制器参数,提高系统鲁棒性和控制精度;利用改进资源分配网络(IRAN)和梯度下降法进行离线学习和在线学习,提高RBF神经网络的运算速度。通过MATLAB仿真实验,相比于传统PID速度控制,神经网络速度控制器具有更高的控制精度,更好的调速性能和鲁棒性。Aiming at the nonlinear,multi parameter variation and system disturbance of PMSM,the traditional PID control was combined with radial basis function neural network which has strong self-adaptive and self-learning ability to design a neural network speed controller of PMSM.RBF neural network was used to adaptively adjust the PID speed controller parameters to improve the system robustness and control accuracy;improved resource allocation network(Iran)and gradient descent method were used for offline learning and online learning to improve the operation speed of RBF neural network.Through MATLAB Simulation experiments,compared with the traditional PID speed control,the neural network speed controller has higher control accuracy,better speed regulation performance and robustness.

关 键 词：永磁同步电机 径向基函数神经网络 改进资源分配网络学习算法 梯度下降法 PID控制器 

分 类 号：TM351[电气工程—电机] TM464