Intelligence Control Based on Self-Correcting Fuzzy-Neural Networks for Brushless DC Motor Drive

It presents a self-correcting fuzzy-neural networks controller (SCFNNC) to realize the requirement of the start,regulating the speed,resisting disturbance and the brake for brushless DC motor.The SCFNNC implements good controlling rules by the method of regulating self-conrrecting gain parameters for the system.The paper emphatically develops designing method of self-correcting gain parameters,the design of fuzzy controller and the method to realize the rules of fuzzy control by artificial neural networks etc.The self-correcting gain parameters depend on expected values of the overshoot,steady-state error and dynamic velocity-drop for the system.According to systematic requirement fuzzy controller is designed,suitable rule table of fuzzy control is also determined,which is used to training neural networks.In order to reaching optimal performances complex control of combining self-correcting fuzzy-neural networks with Bang-Bang and proportional control is applied.It is shown by digital simulation that the system with SCFNNC has excellent dynamic and static performances,good robustness and ability for resisting disturbance.