Stator Flux Linkage Observer Based on RBF Neural Network for IM

Abstract

Direct Torque Control (DTC) is a high performance control method. The stator flux observer is a key part in the method. The accuracy of the stator flux estimation directly affected the performance of DTC. The traditional induction motor stator flux observation method have been analyzed in This paper. And for the shortcomings of existing methods, a on-line identification methods based on Radial Basis Function (RBF) have been proposed in the paper. First, the type reference model to flux identification has been established according to induction motor u-n mathematical model under the static coordinate system. Then, a RBF neural network can be constructed on this basis. After self-organization learning, on-line identification of stator flux can be realized in the RBF neural network. System simulation has been carried out in Matlab/Simulink. The results show that the identification method based on the RBF Neural network can improve the induction motor stator flux measurement accuracy, reduce the impact from the interference factors in observation process and the structure is very simple.