直流無刷馬達之T-S模型鑑別 與控制器設計

Identification and Controller Design of A Brushless DC Motor Based on Takagi–Sugeno Fuzzy Model

吳坤皇(Kun-Huang Wu)

指導教授 ： 賴玲瑩

中原大學/工學院/電機工程研究所/碩士(2004年)

https://doi.org/10.6840/cycu200400351

摘要

本文提供一個利用T-S模糊模型的鑑別方法來對直流無刷馬達位置控制系統進行系統鑑別（system identification），再據此鑑別之數學模式（model）進行模糊控制器（Fuzzy Logic Controller；FLC）設計。雖然FLC的設計並不需要先知道受控體（plant）的數學模型，但因為本論文是以模擬方式來作系統設計，所以得先獲得系統模擬所需的數學模型。
本文之模糊鑑別係利用適應性基因演算法(Adaptive Genetic Algorithm；AGA)進行T-S模型系統尺規因子（scaling factors）、前件部及後件部參數的鑑別工作，使馬達閉迴路和待鑑別的T-S模型系統閉迴路輸出之誤差平方和(Sum of Square Error；SSE )為最小，以獲得馬達系統的等效數學模式，經模擬T-S模糊鑑別與轉移函數鑑別的結果比較，證實前者有較小絶對誤差和（Sum of Absolute Error；SAE）。
傳統二維FLC的輸入常為閉迴路響應的誤差和誤差變化率，本文控制架構保留前者，但後者直接由FLC的輸出端回授取代之，並新增一累加器使其具有的自校正效果，而控制法則同樣利用AGA將FLC的規則庫及尺規因子同時納入搜尋，以獲得最佳控制器參數，最後利用不同的輸入信號及加入雜訊造成系統參數擾動的情況下，來驗證本文控制架構的可行性及強健性。

關鍵字

適應性基因演算法 ； 系統鑑別 ； 自校正 ； T-S模糊模型 ； 最佳控制 ； 模糊控制

並列摘要

This thesis provides a T-S fuzzy model identification method for position control system of DC brushless rotary motor. Then according to the identified model, the fuzzy logic controller (FLC) is designed. Although the FLC design does not need to know the plant model, however, in simulation, a model is needed for its response.
In identification, an adaptive genetic algorithm (AGA) is used for optimal searching of the scaling factors, the antecedent and consequent parameters of the T-S fuzzy model. The optimal one is to minimize the sum of square error (SSE) between the closed-loop outputs of the real motor system and the T-S model. The simulation result shows that the identification using the T-S fuzzy model, when compared to the transfer function model, has smaller sum of absolute error (SAE).
Usually, the two inputs of a traditional fuzzy controller are the closed-loop system output error and its error rate. In this thesis, the former is reserved, but the latter is replaced by the output of FLC. An accumulator is added to enable the self-adjustment ability. In FLC design, the AGA is used to search for the optimal rule base and scaling factors simultaneously. Finally, system parameter uncertainties are simulated through different choice of input signals and adding of output noises. Thus the feasibility and the robustness of the proposed FLC is verified.

並列關鍵字

optimal control ； self-adjustment ； adaptive genetic algorithm ； system identification ； fuzzy logic control ； T-S fuzzy model

參考文獻

[31] 董義雄，「直流無刷馬達之適應性模糊滑動模式位置控制」，中原大學電機工程研究所碩士論文，民92年。

[38] 陳愈仁，「直流無刷馬達模型參考模糊位置控制」，中原大學電機工程研究所碩士論文，民92年。

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