| Brushless DC Motor has overcome a series of problems brought by brush friction ofmotor and has got the advantage of simple structure, reliability, high efficiency and it iseasy to maintain. It is widely used in many aspects of human life. The control system ofbrushless DC motor has multi-variable and is non-linear. As the modern society develops,the traditional PID control method is now hardly able to meet the demand of precisecontrol. The research on brushless DC motor has become a hot topic of motor control field.Fuzzy control theory with its unique advantages has been well applied in the field of thecontrol of brushless DC motor. But fuzzy control has got a little problem when it goes toparameter self-adaptation and self-learning. Therefore, the thesis applied the method offuzzy neural control theory which combines the advantage of self-learning in neuralnetwork control with fuzzy control into the control of brushless DC motor.This thesis mainly studies the problems towards the application of intelligentalgorithm in brushless DC motor. It started from the development of brushless DC motorsand introduced the working principles of brushless DC motors, its composition andmathematic models, and on this basis, a double-loop control system of brushless DCmotor is modeled and simulated using Matlab. Then presented the overview of fuzzycontrol and the structure of fuzzy controller and applied fuzzy control into the controlsystem of brushless DC motor. At last, aimed at the shortcomings of fuzzy control,proposed the control method of fuzzy neural network and gave a brief introduction of theproposed fuzzy neural network and a system planted with this algorithm was simulatedand compared with those planted with the traditional PID and fuzzy control method.The results show that the intelligent algorithms of fuzzy control and fuzzy neuralcontrol methods have better control effect than the traditional PID method. The use ofintelligent algorithm brings better dynamic performance. |
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