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研究生: 彭景詮
Peng, Jing-Quan
論文名稱: 應用適應性類神經網路於機械手臂之追跡控制器設計
Design an Adaptive Neural Network Controller for Robot Manipulator Tracking Control
指導教授: 陳美勇
Chen, Mei-Yung
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 60
中文關鍵詞: 類神經網路適應控制機械手臂
英文關鍵詞: Neural Network, Adaptive Control, Robot Manipulator
DOI URL: http://doi.org/10.6345/NTNU202001285
論文種類: 學術論文
相關次數: 點閱:118下載:2
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  • 本論文研究目的為使用類神經網路(Neural Network)估測機械手臂之未知系統參數,並使用適應性控制(Adaptive Control)作為類神經網路之權重值調變,使機械手臂在未知系統參數的情況下完成追跡。
    在運動學方面使用D-H(Denavit-Hartenberg)座標系統定義並以此推導出正向運動學,在此定義基礎上使用Pieper’s Solution推導出機械手臂的逆向運動學,藉由順向與逆向運動學求出機械手臂末端點在空間中的三維座標與各軸馬達移動角度之間的關係。
    在控制器設計上使用背推(Backstepping)方法設計,將系統分成一個非線性二階系統,設計一個虛擬控制器用以對抗系統未知項,並藉由穩定性分析在保證子系統穩定的狀況下設計該虛擬控制器的形式。對於未知系統參數與系統未知項使用類神經網路進行估測,並藉由適應控制的更新律對類神經網路之權重值做參數調變,藉由Lyapunov 函數與Barbalat引裡證明整個系統的穩定性,最後經由實驗驗證此控制器的性能。

    In this study, we design an adaptive neural network controller that is applied on tracking control for robot manipulator. The neural network is used to estimate the unknown system parameters and combine the adaptive control which is used to update the weight value of neural network.
    The kinematics of robot manipulator is defined by D-H coordinate method and derive forward kinematics. Based on D-H coordinate method, the inverse kinematics is derived by Pieper’s solution. By forward kinematics and inverse kinematics, the relationship between motor rotation angle and the end point position coordinate in three-dimensional space of robot manipulator is described.
    The controller is designed by backstepping method, we divide the system into a nonlinear second-order system, and design the virtual controller which is defined by Lyapunov method to maintain the system stable. For the unknown system parameters and uncertainty, a neural network is used to estimate, and the weight value of neural network is adjusted by adaptive update law. The stability of system is proofed by Lyapunov function and Barbalat’s Lemma. Consequently, the experiment results show performance of this controller in robot manipulator.

    第一章 緒論 1 1.1前言 1 1.2文獻回顧 2 1.3研究動機與目的 7 1.4本論文之貢獻 7 1.5 論文架構 8 第二章 運動學理論基礎 9 2.1 D-H 座標系統定義 10 2.2 正向運動學 12 2.3 逆向運動學 17 第三章 系統控制器理論及設計 23 3.1 控制器理論基礎 23 3.1.1適應性控制(Adaptive Control) 23 3.1.2拉格朗日方程式(Lagrange equation) 24 3.1.3類神經網路控制(Neural Network Control) 24 3.1.4 Lyapunov 理論 26 3.2 適應性類神經網路控制器設計 27 3.2.1基於backstepping之控制器設計 27 3.2.2類神經網路控制器 29 3.2.3系統穩定性分析 30 第四章 實驗設備 33 4.1串聯式機械手臂 33 4.2驅動器 34 4.3運動控制卡 36 4.4資料擷取卡 36 4.5軟體 37 第五章 模擬與實驗結果 38 5.1數值模擬 38 5.2以PI控制器實作於機械手臂之追跡實驗 41 5.2.1 矩形追跡作圖 41 5.2.2 圓形追跡作圖 42 5.2.3 四葉草形追跡作圖 44 5.3類神經網路參數調變實驗 45 5.3.1第六軸類神經網路參數調變 46 5.3.2第六軸適應律倍率調變 48 5.3.3第六、五軸類神經網路控制器與PI控制器之比較 52 第六章 結論與未來展望 57 參考文獻 58

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