臺灣博碩士論文加值系統

本研究在一個二軸平面的機械手臂實驗平台上，進行控制器與錯誤診斷的整合測試。在控制器的部分，使用非追蹤型卡曼濾波器(UKF)進行角速度估測，將此估測與透過差分所得到的角速度，分別代入計算扭力控制器演算法觀察其效果差異。此外將提出一個僅需位置回授的新型控制器與上述兩個控制器一起比較。在錯誤偵測與判別中，實現二階廣義偽貝氏演算法(GPB-2)及交互多模型(IMM)錯誤演算法的測試，其中所使用之估測器為控制器部分的UKF估測器。由實驗結果可以看出，僅需位置回授的新型控制器在只有位置回授的情形下，效能更勝傳統的計算扭力控制器，而錯誤偵測與判別系統也能夠在系統發生錯誤後的極短時間內，告知使用者系統已經發生錯誤，並判斷出錯誤發生的位置，不僅增加了安全性，也降低硬體成本，增加未來智慧型機器人能夠同時擁有安全與效能兼備的可能性。

This study experimentally evaluates the performance of various kinds of controllers and actuator fault diagnosis system on a two-joint robotic manipulator. On the part of controllers, we use UKF(Unscented Kalman Filter) to estimate (angular) velocities, and compare the performance of the computed-torque controllers with velocities estimated by UKF and backward difference filters respectively. Moreover, we propose a new control method which only requires position feedback. On the part of actuator fault diagnosis system, we combine UKF , GPB-2(General Pseudo Bayesian of Order 2) and IMM(Interacting Multiple Model) algorithms to detect and identify actuator failures of the robotic manipulator. The experimental results show that the new control method has better performance than other controllers. The results also show that the fault diagnosis system can correctly detect and isolate actuator failures in a short time.

摘 要 I
英文摘要 II
目錄 III
圖目錄 V
表目錄 VIII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 論文貢獻 2
1.3 論文架構 2
第二章 相關研究 3
2.1 機器手臂控制相關研究 3
2.2 機器人錯誤偵測與判別相關研究 4
第三章 機械手臂動態模型與控制器設計 6
3.1 機械手臂的動力學模型 6
3.2 計算力矩控制器 10
3.3 觀察器設計 11
3.4 僅需位置回授的新型控制器 15
3.4.1 線性控制器的設計 17
3.4.2 非線性控制器的設計 20
第四章 錯誤偵測與判別技術 25
4.1 以模型為基礎的錯誤偵測技術 25
4.2 多模型架構 27
4.2.1 廣義偽貝氏法 31
4.2.2 交互多模型法 34
4.3 階層式錯誤偵測與判別系統 37
第五章 實驗結果與討論 40
5.1 機械手臂硬體實驗平台 40
5.2 控制器實驗結果 45
5.2.1 UKF估測器效果測試 45
5.2.2 計算力矩控制器實驗結果 48
5.2.3 僅需位置回授的新型控制器實驗結果 55
5.2.4 性能分析與比較 59
5.2.5 強健性測試 61
5.3 錯誤偵測實驗結果 65
5.4 錯誤判別實驗結果 69
第六章 結論及未來工作 80
6.1 結論 80
6.2 未來工作 81
參考文獻 82

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