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研究生:黃仲廷
研究生(外文):Chung-Ting Huang
論文名稱:應用類神經網路抑制順滑模式切跳問題於四軸機械手臂軌跡追蹤之控制器設計
論文名稱(外文):A Neural-Network-Sliding-Mode Controller Design for a 4-DOF Manipulator Tracking with Chattering Reduction
指導教授:蘇武昌
指導教授(外文):Wu-Chung Su
口試委員:范志鵬張勝岳
口試委員(外文):Chih-Peng FanSheng-Yueh Chang
口試日期:2021-07-28
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:73
中文關鍵詞:可變結構控順滑模式控制類神經控制人工神經元Lyapunov函數機械手臂
外文關鍵詞:variable structure controlneural network controlauto-tuning neuronsliding mode controlLyapunovmanipulator
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本文研究主要在於將類神經控制(neural network control) 結合順滑模式控制(sliding mode control) 並應用於機械手臂的控制器設計。由於現實環境中,在設計控制器時往往會遇到系統的不確定量或外部干擾問題,例如雜訊、未知的負載等相關因素,因此本研究選用了順滑模式控制器為主控制器,其優點在於處理上述的控制問題時具有良好的強健性,然而控制器中的切換項卻會造成切跳問題(chattering),在實際應用時會使的控制元件有不必要的損耗,故本篇將導入類神經控制作為輔助,降低切換增益(switching gain),減緩切跳問題。

本篇論文導入了RBF-類神經網路控制來控制系統內的響應,另外再以一種藉由調整其激活函數的斜率、閥值來改變控制輸出的單一類神經元根據與順滑層的距離和強健控制項sign(s)做調變,達到抑制切跳問題的效果,最後經過了Lyapunov理論證明了系統穩定性,並透過模擬驗證控制器的性能。
In this study, we design a sliding mode controller for a 4-dof manipulator which is applies on trajectory tracking. Sliding mode control (SMC) has good robustness for uncertainty problem and external disturbance. However, there is always chattering issue in sliding mode control cause by switching gain, which will cause unnecessary loss of control components in practical applications. Therefore, we introduce the neural network control and hope that can reduce the switch gain for chattering in SMC.
Consequently, we choose the radial basis function (RBF) neural network with SMC controller to be major one for robotic system response. Secondly, we introduce an auto-tuning single neuron which can self-tuning the slope and bias of activation function. Then we combine an auto-tuning single neuron with RBF-SMC by a decision law, so that can reduce the switch gain for chattering issue. We also analyze the stability of the system by Lyapunov function, and show the excellent performance from the simulation result.
誌謝 i
摘要 ii
Abstract iii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 論文架構 2
第二章 機械手臂運動學理論 4
2.1 機械手臂運動學 4
2.1.1 Denavit-Hartenberg 表示法 5
2.1.2 順向運動學 7
2.1.3 逆向運動學 11
2.2 機械手臂動態系統方程式 15
2.2.1 Lagrange Equation 15
第三章 控制器理論 24
3.1 順滑模式控制 24
3.2 類神經網路控制 27
第四章 控制器設計 31
4.1 順滑模式控制器設計 31
4.2 單一類神經元控制器設計 34
4.3 類神經網路結合順滑模式控制器設計 36
4.4 切換法則 40
第五章 模擬結果及討論 44
5.1 模擬軟體工具 44
5.2 模擬結果 44
5.2.1 順滑模式控制 45
5.2.2 順滑模式結合單一類神經元控制器 53
5.2.3 RBF類神經網路結合順滑模式與單一類神經元控制器 61
5.2.4 機械手臂末端軌跡繞行實驗 69
第六章 結果討論與未來展望 71
參考文獻 72
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[10]Z. Chen, and J. Gu, ‘‘RBF-Neural-Network-Based Adaptive Robust Control for Nonlinear Bilateral Teleoperation Manipulators with Uncertainty and Time Delay,’’ IEEE/ASME Transactions on Mechatronics., vol. 25, no. 2, April 2020.
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[14]J. Y. Hung et al., “Variable structure control: A survey,” IEEE Trans. Ind. Electron., vol. 40, pp. 2–22, Jan. 1993.
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[16]I. Garcia, F. Goncalves, T. Ribeiro, P. Fernandes, C. Rocha, R. Boucinha, G. Lopes, A. F. Riberio, “Autonomous 4DOF Robotic Manipulator Prototype
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