Position Sensorless Control And Single Neural PID Control For Switched Reluctance Motor Based On RBFNN

Due to its simple construction, reliability, high efficiency and low cost, switched reluctance motor (SRM) has shown huge competitive power in many fields. But mechanical position sensors add to the cost, complexity and potential unreliability at high speed and this has motivated the investigation of sensorless position estimation. Because of its high nonlinear electromagnetism characteristic, the sensorless control based on accurate model of SRM is hard to be accomplished. In recent years, artificial neural network (ANN) technology has made a great progress, which gives a new method to accomplish position sensorless control of SRM. A novel control method for SRM using adaptive radial basis function(RBF) network is presented in this paper. For the adopted network, the training data set is comprised of magnetization data of the SRM for which phase current and phase flux linkage are inputs and the corresponding position is the output,through the off-line and the online training, the ANN can build up a correlation among phase current, phase flux linkage and position, thereby facilitating elimination of the rotor position sensor. Conventional PID control is widely adopted in many fields because of its simple structure, high reliability and easily implementation. PID controller has good control effect if the parameters of system model have not big variation, but when controlled object has high nonlinear trait and high uncertenty, PID control can't achieve good result. For SRM, it has high nonlinearized trait, PID controller with fixed parameter can't achieve good performance index. This paper presents a novel approach of single neuron adaptive control for SRM based on 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, and implements self-learning of controller's parameters by single neuron controller, thus achieve on-line regulation of controller's parameters. The simulation and experimental results show that adaptive RBF network can achieve correct phase conversion and thus positon sensorless control of SRM is achieved; adaptive single neuron control based on RBF neural network on-line identification can achieve on-line identification and on-line control with high control accuracy and good dynamic characteristics.