Sensorless vector control of induction motor using artificial neural network

This paper presents a novel approach to sensorless vector control of induction motor drives. The method is based on an adaptive flux observer in the rotor-speed reference frame, in which an artificial neural network (ANN) is employed to modify the estimated rotor flux to improve the performance of speed estimation. The adopted ANN is a feedforward neural network identified off-line. It uses the backpropagation learning process to update the weights. The data for training are obtained from a computer simulation and experimental data file of a vector control system. Then, the estimated rotor flux is used in the speed estimation that will feed back to the vector control system, The proposed method has the advantages of better accuracy at low speed range and speed following under heavy loads. The whole system is implemented in a TMS320C30 DSP chip and experimental results show the effectiveness of the proposed method