直流無刷馬達模型參考模糊位置控制

Model Reference Fuzzy Position Control of a DC Brushless Motor

陳愈仁(Yu-Jen Chen)

指導教授 ： 賴玲瑩

中原大學/工學院/電機工程研究所/碩士(2003年)

https://doi.org/10.6840/cycu200300102

摘要

本論文之主要目的在於將模型參考模糊控制器（Model Reference Fuzzy Controller，簡稱MRFC）應用於直流無刷馬達位置控制上。由於直流無刷馬達本身具有高階、非線性與內部參數有隨環境的不同而改變其值之特性；而模糊控制器（FC）具有高度強健性、且對設計不需要預知未知受控系統的模型；因此FC適合於控制直流無刷馬達。傳統模糊控制器的輸入常使用閉路系統的誤差及誤差變化率，本文保留前者，但後者以參考模型與受控體兩者的輸出誤差取代之，因此稱之MRFC。
一般而言，模糊控制器之規則庫（rule base）和尺規因素（scaling factors）對閉路系統的響應、收斂性以及穩定性都有決定性的影響。規則庫及尺規因素通常是以經驗法則來選取或由試誤法來設定。為使其達到最佳化之目的，本文則以適應性基因演算法（Adaptive Genetic Algorithm，簡稱AGA）來調適MRFC的規則庫及尺規因素。經由模擬與實驗皆證實，AGA最佳化之MRFC與AGA最佳化之PID控制比較結果，前者有較佳的響應。

關鍵字

適應性基因演算法 ； 參考模型 ； 模糊控制 ； 直流無刷馬達

並列摘要

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.

並列關鍵字

Fuzzy Control ； Reference Model ； Adaptive Genetic Algorithm ； DC Brushless Motor

參考文獻

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