臺灣博碩士論文加值系統

本研究以發展多功能機械臂為目的，設計出以氣動人工肌肉缸為主要致動器之多自由度機械臂，具有穿戴功能；當穿戴機械臂時，其自由度為四，可作為上肢復健輔具，執行復健運動任務，各旋轉軸由氣動人工肌肉缸來驅動，可確保人機互動時之安全性及可撓性；當結合手端伺服機時，可作為七自由度之冗餘機械手臂，搭配夾爪可執行點到點之物件抓取動作。
系統整合方面，由myRIO整合多個氣壓控制閥與感測器執行氣動人工肌肉缸之控制，並透過LabVIEW程式，建構UI介面進行整體系統整合；撰寫控制器程式，透過機械臂之運動學規劃出復健軌跡命令，建立閉迴路控制系統使機械臂達到復健軌跡追蹤之任務。控制方面，首先以模擬和實驗證明PID控制氣動人工肌肉之可行性，為了增加機械臂於復健追跡任務之精確性，本研究導入反覆式學習控制來改善PID控制器於非線性補償與相位落後問題，使機械臂於往返之復健軌跡下都具有一定的追蹤精度。

The purpose of this study is based on Pneumatic Artificial Muscles (PAMs) to develop a robot for two purposes. The first type is a redundant robot, which has 4-DOFs actuated by PAMs and 3-DOFs actuated by servos. The second type is a wearable exoskeleton robot, which is used for upper limb rehabilitation and is only actuated by PAMs. The advantages of using PAMs are making the robot’s joints have flexibility and improving the safety for rehabilitation.
For real-time implementation, we used an embedded controller (myRIO, made by National Instruments) to integrate the robot with sensors. Because the dynamics of PAM is nonlinear and complicated due to the nonlinear friction, a Proportional-Integral-Derivative (PID), Least Mean Square (LMS)-PID and Iterative Learning Control (ILC) have studied for the specified trajectory tracking tasks. Compared from the other two controllers, ILC has the best performance for repetitive trajectory tracking tasks.

中文摘要 i
英文摘要 ii
誌　謝 iii
目 錄 iv
表目錄 vi
圖目錄 viii
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.2.1氣動人工肌肉缸之發展與應用 2
1.2.2氣動人工肌肉缸之控制文獻 5
1.2.3反覆式學習控制之發展與應用 6
1.3 論文架構 7
第二章 機械臂設計與系統架構 8
2.1 機構設計介紹 8
2.1.1 人體上肢介紹 8
2.1.2 機械臂設計 10
2.2機械臂之運動學 17
2.2.1 機械臂之順向運動學 18
2.2.2 冗餘機械臂之反向運動學 22
2.3 機電系統介紹 25
2.3.1 機電系統設計 25
2.3.2 硬體介紹 26
2.3.2.1 嵌入式控制器 26
2.3.2.2 氣動人工肌肉缸 27
2.3.2.3 比例式方向控制閥 27
2.3.2.4 比例式壓力調節閥 29
2.3.2.5 壓力感測器 29
2.3.2.6 旋轉編碼器 30
2.3.2.7伺服機 30
2.3.3 軟體介紹 32
2.4 機械臂旋轉軸驅動方式介紹與PAM選擇 33
第三章 單軸機械臂之動態模型與控制器設計 36
3.1 單軸機械臂之動態模型 36
3.2 機械臂之控制器設計 41
3.2.1 PID控制器 41
3.2.2 自適應PID控制器 43
3.2.3 反覆式學習控制系統 46
第四章 動態模型參數鑑別與PID控制模擬 49
4.1 單軸機械臂之開迴路實驗 49
4.2 單軸機械臂之模型參數鑑別 51
4.2.1 實數型基因演算法 51
4.2.2 單軸機械臂之模型參數鑑別結果 56
4.3 PID控制模擬與PID參數鑑別 59
第五章 單軸機械臂之控制器實驗與探討 62
5.1 PID控制器實驗 62
5.2 自適應PID控制器實驗 64
5.3 反覆式學習控制器實驗 68
5.4 機械臂各軸之平均速度實驗 72
第六章 多軸復健軌跡下機械臂之控制實驗 74
6.1 復健軌跡規劃 74
6.1.1 基於Labview程式下建立機械臂運動學 74
6.1.2 復健軌跡規劃 76
6.2 復健軌跡下之多軸機械臂追跡實驗 80
6.3 機械臂加入負載實驗 92
6.4 雙軸同動復健軌跡實驗 102
第七章 結論及未來展望 110
參考文獻 112

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