Application of CMAC neural network to the control of induction motor drives

In this paper, a cerebellar-model-articulation-controller (CMAC) neural network (NN) based control system is developed for a speed-sensorless induction motor that is driven by a space-vector pulse-width modulation (SVPWM) inverter. By analyzing the CMAC NN structure and motor model in the stationary reference frame, the motor speed can be estimated through CMAC NN. The gradient-type learning algorithm is used to train the CMAC NN online in order to provide a real-time adaptive identification of the motor speed. The CMAC NN can be viewed as a speed estimator that produces the estimated speed to the speed control loop to accomplish the speed-sensorless vector control drive. The effectiveness of the proposed CMAC speed estimator is verified by experimental results in various conditions, and the performance of the proposed control system is compared with a new neural algorithm. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN.