Design and simulation of flux identification based on RBF neural network for induction motor

Direct Torque Control (DTC) is a high performance induction motor control method. However, the accuracy of the stator flux estimation is directly related to induction motor control performance. 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 reference model of flux identification should be 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, online 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.