Speed control of a brushless DC motor drive via adaptive neuro-fuzzy controller based on emotional learning algorithm

Principle of a new adaptive neuro-fuzzy controller (NFC) is introduced and is used speed control of brushless DC (BLDC) 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/or torque control of a BLDC 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.