Adaptive Neuro-Fuzzy Intelligent Controller via Emotional Learning for Indirect Vector Control (IVC) of Induction Motor Drives

Abstract

The principle of a new adaptive Neuro-Fuzzy Controller (NFC) is introduced and used for indirect vector control of induction motor drives. The proposed algorithm has advantages of neural and fuzzy networks and uses a supervised emotional learning process to train the NFC. This newly developed design leads to a controller with minimum hardware and improved dynamic performance. System implementation is relatively easy since it requires less calculation as compared with the conventional fuzzy and/or neural networks, used for electrical drive applications. The proposed controller is used for speed and torque control of an induction motor drive. In order to demonstrate the NFC ability to follow the reference speed and to reject undesired disturbances, its performance is simulated and compared with that of a conventional PID controller.