Indirect field oriented adaptive control of induction motor based on neuro-fuzzy controller

The present paper proposes an adaptive structure, completely based on the artificial intelligence concepts, for speed control of an induction motor, without any identification of the motor dynamic. Approach with reference model has been chosen, and a neuro-fuzzy controller assures excellent qualities in terms of tracking, and disturbance rejection with high robustness. A neural adaptive mechanism is synthesized to correct the law generated by the controller to provide a compensation signal. This last, added to the controller output, generate the appropriate adapted law. The effectiveness and feasibility of the structure developed is verified by several simulation tests with different conditions operating.