A novel control method based on wavelet neural networks for vector control of induction motor drives

The motor is the workhorse of industry. The control and identification of induction motor drives using artificial intelligence is the key point for high performance electrical driving. A new architecture of nonlinear autoregressive moving average model based on wavelet neural networks is presented for enhancing the performance of induction motor. The Akaikepsilas final predication error criterion is applied to select the optimum number of wavelets to be used in the WNN model. By two-phase synchronously rotating reference frame transformation, an induction motor can be controlled like a separately excited dc motor. The WNN controller is utilized as speed controller to control the torque by the quadrature axis of the stator current. The WNN controller can be trained well. Theoretic analysis and simulations show that the novel method is highly effective.