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研究生:曹家瑞
研究生(外文):Cao, Jia-Rui
論文名稱:利用T-S模糊模型於二輪自體平衡車之控制器設計
論文名稱(外文):Stabilizing Controller Design Using Fuzzy T-S Model on Two Wheeled Self-Balancing Vehicle
指導教授:黃志鵬黃志鵬引用關係
指導教授(外文):Huang, Chih-Peng
口試日期:2016-07-12
學位類別:碩士
校院名稱:臺北市立大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:46
中文關鍵詞:T-S 模糊模型基因演算法二輪自體平衡車狀態回授增益值
外文關鍵詞:Fuzzy T-S ModelGenetic AlgorithmTwo Wheeled Self-Balancing VehicleState Feedback Gain
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在此篇論文中,我們基於Fuzzy T-S Model (T-S Fuzzy)結合基因演算法(Genetic Algorithm, GA)提出建置於二輪自體平衡車(Two Wheeled Self-Balancing Vehicle, TWSBV)的控制器設計。
為建置二輪自體平衡車的穩定控制器,我們使用基因演算法來尋找較為合適的狀態回饋增益值(State Feedback Gains),利用兩個歸屬函數:身體角度與身體角速度所組成的T-S Fuzzy Model,來達成二輪自體平衡車的非線性控制。透過二輪自體平衡車的狀態方程式,可以產生符合該狀態的脈衝響應,將狀態增益值代入其中,便可分析脈衝響應(Impulse Response)的性能指標(Performance Index),透過性能指標的數值表現,評估該組狀態增益值是否有較好的表現。
在本研究中,基因演算法的適應性函數由脈衝響應之性能指標所構成,透過適應性函數所得出的適應值,可以判斷出何者為較佳的狀態增益值。並藉由基因演算法的物競天擇能力,能夠有效的減少搜索狀態增益值所需要的時間。結合調整後的狀態增益值及T-S模糊規則,我們可以建置出結合基因演算法的T-S模糊控制器。
本論文於最後一章展示結果,透過與其他方法做模擬比較之方式,在結果呈現上,本研究所提出的方法減少了達到穩定平衡的所需時間,而平衡前的震盪也被有效地降低。

In this research, we proposed a controller design method of the Two Wheeled Self-Balancing Vehicle (TWSBV) based on Fuzzy T-S Model (T-S Fuzzy) associated with genetic algorithm (GA). To achieve the stable controller of TWSBV, we used GA to properly seek for the feasible state feedback gains for the T-S Fuzzy controller, which is constructed by the heuristic experiment with two fuzzy membership functions of vehicle body angle and vehicle angular velocity to conquer some nonlinear parameters. With the convergent state feedback gains via GA, we can ensure the TWSBV has the better performances. Through analyzing the TWSBV impulse response characteristics produced from the state-space equation of TWSBV dynamic model, the system’s performance of the controllers can be evaluated. The fitness function of genetic algorithm is formulated by some performance indexes so that we can search for better state feedback gains. Furthermore, using GA’s ability of natural selection can reduce the tuning time of the satisfying state feedback gains. By the well-tuned state feedback gains, we thus can achieve a fuzzy model controller of the TWSBV system. The experimental simulation demonstrates that the proposed method has less balancing time and states’ oscillation, which mean that it indeed has better performance than others.
中文摘要 I
英文摘要 II
目次 III
圖目次 IV
表目次 VI
第一章 緒論 1
第一節 研究背景 1
第二節 研究目的 3
第三節 論文架構 4
第二章 文獻探討 5
第一節 二輪平衡車之控制器 5
第二節 模糊理論應用於二輪自體平衡車控制器 7
第三節 模糊控制器參數最佳化 9
第三章 研究方法 11
第一節 使用平台介紹 11
第二節 系統模型推導 14
第三節 模糊規則設計 17
第四節 狀態增益值與模糊規則的對稱關係 19
第五節 基因演算法 23
第六節 模糊規則與基因演算法的結合 27
第七節 基因演算法之上下界數值 30
第四章 模擬與實驗結果 31
第一節狀態增益值的合成比較 31
第二節 效能比較 34
第三節 實機測試結果 39
第四節 結論與建議 40
論文參考文獻 41
網頁參考文獻 45
附錄 46

論文參考文獻

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網頁參考文獻

[31] Segway Human Transporter。線上檢索日期: 2016年3月28號。
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[32] HTWay: A Hitechnic Segway type balancing robot。線上檢索日期: 2016年3月28號。網址: http://www.hitechnic.com/blog/gyro-sensor/htway/
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[35] NXT伺服馬達。線上檢索日期: 2016年3月28號。
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[36] 輪子與地板間的摩擦係數。線上檢索日期: 2016年3月28號。
網址: http://philohome.com/traction/traction.htm

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