基于神经网络的开关磁阻电机无位置传感器控制

论文提出了基于自适应径向基函数(radial basis function，RBF)神经网络的开关磁阻电机(SRM)无位置传感器控制新方法。该方法构造了一个隐层节点初始个数为零的RBF网络，通过在训练过程中不断按照自适应算法添加和删除隐层单元，形成一个结构简单、紧凑的网络来实现电机电压、磁链与转子位置之间的非线性映射，实现SRM的无位置传感器控制。网络训练分为离线训练和在线训练两个部分。利用训练样本按给出的自适应算法对网络进行离线训练，确定RBF网络隐层节点的个数及位置；按递推最小二乘法(RLS)在线修正隐层与输出层之间的连接权。仿真及实验结果表明，该方法能够实现电机的准确换相，从而实现了位置传感器的消去。

POSITION SENSORLESS CONTROL FOR SWITCHED RELUCTANCE MOTORS USING NEURAL NETWORK

This paper presents an approach of position sensorless control for switched reluctance motors (SRM) based on an adaptive radial basis function (RBF) neural network. In the proposed RBF neural network, there is no hidden units at the beginning, and during the process of learning, they are increased or decreased according to an adaptive algorithm so that the RBF neural network is built with a much simpler and tighter structure to form an efficient nonlinear map, and then it facilitates the elimination of the position sensors. The RBF neural network is trained both off-line and on-line. In the off-line training process with the training data, the number and locations of the hidden units of the RBF neural network are obtained; while on-line learning, the weights between the hidden layer and the output layer are updated according to the recursive least squares (RLS) algorithm. The simulation and experimental result shows that this method can achieve correct phase conversion, and thus the sensorless control of SRM is achieved.