JEE_V4_N1_RP1 Azeddine Chaiba Rachid Abdessemed M. Lokmen Bendaas Journal on Electrical Engineering 2230 – 7176 4 1 8 15 Doubly-Fed Asynchronous Motor (DFAM), Torque Tracking Control, Neuro-Fuzzy controller In this paper neuro-fuzzy controller for Doubly Fed Asynchronous Motor (DFAM) drive is proposed. First, a mathematical model of DFAM written in an appropriate d-q reference frame is established to investigate simulations. In order to control the rotor currents of DFAM, a torque tracking control law is synthesized using PI controllers, under conditions of the stator side power factor controlled at unity level. A four layer Neural Network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performances of neuro-fuzzy controller (NFC) which is based on the torque tracking control algorithm are investigated and compared to those obtained from the PI controller. Results obtained in Matlab/Simulink environment show that the NFC is more robust, has superior dynamic performance and hence found to be a suitable replacement of the conventional PI controller for the high performance drive applications. July - September 2010 Copyright © 2010 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=1246