Model Reference Fuzzy Position Control of a DC Brushless Motor

• Main Authors: Yu-Jen Chen, 陳愈仁
• Other Authors: Lin-Ying Lai
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/872695

碩士 === 中原大學 === 電機工程研究所 === 91 === The main purpose of this thesis is to study the design of a Model Reference Fuzzy Controller (MRFC) for position control of a DC brushless motor. A DC brushless motor is a high order and nonlinear system whose internal parameter values vary with different environments. On the other hand, a fuzzy controller is highly robust and its design needs no prior knowledge of controller system model. Therefore, it is suitable to use a fuzzy controller for a DC brushless motor system. Usually, the two inputs of a traditional fuzzy controller are the close-loop system error and error rate. In this thesis, the former is reserved, but the latter is replaced by the error between outputs of a reference model and the plant. So it is called MRFC. In general, the rule base and the Scaling factors of fuzzy controller have decisive effects for response, convergence and stability of the closed-loop system. The rule base and the Scaling factors are usually chosen by experience or by trial-and-error. To optimize the MRFC, this thesis uses Adaptive Genetic Algorithm (AGA) to adjust the rule base parameters and the scaling factors of MRFC, simultaneously. Both the results of the simulation and the experimental show that the response of the MRFC optimized by AGA is better than the response of the PID controller optimized by the same AGA.