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

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.