基于RBF神经网络的开关磁阻电机单神经元PID控制

SINGLE NEURON PID CONTROL FOR SWITCHED RELUCTANCE MOTORS BASED ON RBF NEURAL NETWORK

论文提出了一种基于径向基函数(radialbasisfunction)神经网络在线辨识的开关磁阻电机(SRM)单神经元PID自适应控制新方法。该方法针对开关磁阻电机的非线性,利用具有自学习和自适应能力的单神经元来构成开关磁阻电机的单神经元自适应控制器,不但结构简单,而且能适应环境变化,具有较强的鲁棒性。并构造了一个RBF网络对系统进行在线辨识,建立其在线参考模型,由单神经元控制器完成控制器参数的自学习,从而实现控制器参数的在线调整,能取得更好的控制效果。样机的实验结果表明,文中所提出的基于RBF神经网络辨识的开关磁阻电机单神经元自适应PID控制方法,通过在线辨识建立了过程模型并为神经元控制器提供了梯度信息,达到了在线辨识在线控制的目的,控制精度高,动态特性好。

This paper presents an novel approach of single neuron adaptive control for switched reluctance motors （SRM） based on radial basis function （RBF） neural network on-line identification. The method uses single neuron to construct the adaptive controller of SRM, and has the advantages of simple construction, adaptability and robustness. A RBF network is built to identify the system on-line, and then constructs the on-line reference model, implements self-learning of controller parameters by single neuron controller, thus achieve on-line regulation of controller＇s parameters. The experimental result shows that the method given in this paper can construct processing model through on-line identification and then give gradient information to neuron controller, it can achieve on-line identification and on-line control with high control accuracy and good dynamic characteristics.