基于PSO-BP神经网络的无刷直流电机模型及控制系统研究  

Research onModel and Control System of Brushless DC Motor Based on PSO-BP Neural Network

作　　者：程声烽 程小华[1] 杨露[1] 

机构地区：[1]华南理工大学电力学院,广州510640

出　　处：《微电机》2014年第8期44-47,共4页Micromotors

摘　　要：针对无刷直流电机(BLDCM)气隙磁密非线性分布的特性,利用粒子群优化的反向传播(PSO-BP)神经网络对气隙磁密波形进行逼近,从而建立较为精确的BLDCM模型。在此模型基础上设计了一种双闭环控制系统:转速环采用单神经元PID控制,通过改进的Hebb学习规则调整权值,以实现PID三个参数的自适应;电流环采用滞环电流控制,以实现电流调节。在Matlab/Simulink中搭建仿真模型,仿真结果验证了该模型的正确性。For the non-linear distribution characteristic of air gap flux density of brushless DC motor, using particle swarm optimization back propagation neural network to approximate the flux density waveform in or- der to establish a more precise BLDCM model. A double-loop control system was designed based on this model： speed loop used the single neuron PID control, through improved Hebb learning rule to adjust the weights in order to realize the self-adaptation of three PID parameters; current loop used the hysteresis cur- rent control to realize the current regulation. The simulation model based on Matlab/Simulink was built, and the results verified the correctness of the model.

关 键 词：无刷直流电机 气隙磁密 PSO-BP神经网络 单神经元PID 仿真 

分 类 号：TM361[电气工程—电机] TP273[自动化与计算机技术—检测技术与自动化装置]