Single neural PID control for sensorless switched reluctance motor based on RBF neural network

This paper presents a novel approach of single neuron PID control for position sensorless switched reluctance motors (SRM) based on 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 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. Moreover, the paper uses single neuron to construct the adaptive controller of SRM, which has the advantages of simple structure, 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 parameters. The experimental result shows that the method given in this paper can construct process 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.