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研究生：	李凱笙 Li, Kai-Sheng
論文名稱：	適應式迭代學習算則於機械臂追跡與循跡控制之應用 Adaptive Iterative Learning Algorithm to Robotic Tracking and Contouring Problems
指導教授：	陳介力 Chen, Chieh-Li
學位類別：	博士 Doctor
系所名稱：	工學院 - 航空太空工程學系 Department of Aeronautics & Astronautics
論文出版年：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	138
中文關鍵詞：	適應式迭代學習控制、追跡控制、循跡控制
外文關鍵詞：	adaptive iterative learning control, tracking control, contouring control
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迭代學習理論是以具重複工作性質的工業機械人為背景所提出，傳統迭代學習控制成功應用之關鍵需有效克服系統的不確定性和具有重複性質的外擾。隨著迭代學習控制的深入研究，其研究內容廣泛包含學習控制律與學習系統的研究，學習的收斂性、學習控制過程的強健性，收斂速度及初值問題等。另一方面，基於傳統迭代學習控制的缺失，近年來適應性學習等控制技術越來越多運用於迭代學習控制，因此產生各種新的迭代學習算則。爲了克服具變動性的外擾問題，不連續的控制手段將引入了適應性迭代學習控制架構，以增加其閉迴路的強健性，並獲得良好的追蹤性能。由於實際的物理限制，高頻率的切換控制難以被實踐且將會導致非期望的控制結果。因此，建構於適應性迭代學習算則的設計理念，本論文將致力於設計無顫振的簡易學習控制架構，以消除嚴重的控制顫振，並確保閉迴路控制系統的穩定性與強健性；此外亦提出新的適應式學習律，使得追蹤誤差可以在迭代軸達到快速收斂的目標。就起始定位問題而言，將引入起始修正技巧來設計追蹤誤差方程式，得以有效的克服可變動的起始誤差。在本文中，除了探討一般機械臂的動態關節追蹤問題外，也針對機械臂末端所在位置的工作空間中，具有不確定性質的順向運動學問題加以研究。最終的目標是將本文的控制理論推展至廣泛具有重複性的機械系統運動控制。

The iterative learning control (ILC) theory is proposed for industry robotic systems under repeatable control environment. The traditional ILC is proposed to handle systems with uncertainties and repeat disturbances. It improves control system performance by repetition learning in a finite time interval. Theoretical analysis, design and applications have been widely exploited. In recent years, learning controller approaches to cope with the disadvantage of traditional ILC in association with other control method, such as adaptive learning technique, are proposed. In adaptive iterative learning control (AILC) schemes, the discontinuous control technique is utilized to deal with varying disturbances and improve response of the closed-loop control system. Due to actual physical limits, the high-frequency switching control is difficult to be realized and also leads to undesired control results. Based on the design concept of AILC, this dissertation devotes to develop a non-chattering learning control scheme to avoid serious control chattering. A simple control scheme to guarantee stability and robustness of the closed-loop system is proposed. Fast convergence of tracking error is achieved with the proposed adaptive learning law in the iteration domain. To deal the initial resetting condition problem in ILC, the error function with initial rectifying action is utilized for the initial error variations. For robotic manipulator tracking problems, tracking controllers not only compensate dynamic uncertainties in the joint-space but also suppress the influence of both kinematic and dynamic uncertainties in the task-space. The results of this dissertation obtained can be applied to motion control of a wide class of dynamic system with repetitive.

ABSTRACT IN CHINESE	i
ABSTRACT	iii
ACKNOWLEDGEMENTS 	v
CONTENTS	vi
LIST OF TABLES	x
LIST OF FIGURES	xi

CHAPTER
I. INTRODUCTION	1
1 Motivation	1
2 Literature review	2
2.1 Review of iterative learning control	2
2.2 Review of adaptive iterative learning control	4
2.3 Review of initial resetting condition problem for iterative learning control	5
3 Research objectives	7
II. ITERATIVE LEARNING CONTROL	10
1 Introduction	10
2 Iterative learning controller Scheme	12
2.1 Previous cycle learning controller (PCL) Scheme	13
2.2 Current cycle learning controller (CCL) scheme	15
2.3 A combination of PCL and CCL in the ILC (PCCL) scheme	16
3 Adaptive iterative learning control (AILC) for a Robotic Manipulator	17
3.1 Adaptive control	18
3.2 Adaptive iterative learning control	21
3.3 Adaptive iterative learning control with composite energy function	23
4 Initial resetting condition problem in ILC	27
4.1 Initial rectifying action in learning controller	27
4.2 Initial rectifying action in error function	28
4.3 Initial rectifying action in desired reference trajectory	29
III. OBSERVER-BASED ROBUST AILC FOR THE ROBOTIC MANIPULATOR SYSTEM	31
1 Introduction	31
2 Mathematical model of the dynamic robotic manipulator system	32
3 The observer-based learning controller Scheme	34
4 Performance and stability analysis of the tracking control System	36
4.1 The non-increasing property of the composite energy function	38
4.2 The boundedness property of the system state and the control signal	40
4.3 The convergence of the tracking error function	42
5 Numerical simulations	44
5.1 Learning for identical initial state errors	46
5.2 Learning for perturbed initial state errors	49
6 Discussions	51
IV. DYNAMIC ITERATION TRACKING CONTROL FOR THE UNCERTAIN ROBOTIC MANIPULATOR	52
1 Introduction	52
2 Problem formulation	53
3 Main result of the proposed robust PI controller design	55
4 Performance analysis of the tracking control System	57
4.1 The non-increasing property of the composite energy function	58
4.2 The boundedness property of the system state and the control signal	59
4.3 The convergence of the tracking error function	61
4.4 The stability analysis of the estimator	62
5 Numerical simulations	63
6 Discussions	70
V. TASK-SPACE TRACKING CONTROL FOR UNCERTAIN ROBOTS BASED ON ITERATIVE LEARNING	71
1 Introduction	71
2 Robot kinematic and problem formulation	72
3 Main result of the proposed adaptive iterative Jacobian tracking controller	76
4 Performance and stability analysis of the task-space tracking control system	78
4.1 The non-increasing property of the composite energy function	79
4.2 The boundedness property of the system state and the control signal	82
4.3 The convergence of the task-space tracking error function	86
5 Numerical simulations	87
6 Discussions	94
VI. ITERATIVE CONTOURING CONTROL FOR ROBOTIC SYSTEMS WITH KINEMATIC UNCERTAINTIES	95
1 Introduction	95
2 Description of contour error: A two dimensional case	96
3 Robust contouring controller design with uncertain kinematic	98
3.1 Problem formulation of contour error dynamic	98
3.2 The proposed robust iterative contouring control scheme	101
3.3 Performance analysis of the robust iterative contouring control system	103
4 Adaptive iterative kinematic contouring control scheme	107
4.1 Main results of the proposed control scheme	107
4.2 Performance analysis of the adaptive iterative kinematic contouring control system	109
5 Numerical simulations	115
5.1 Robust iterative contouring control scheme	116
5.2 Adaptive iterative kinematic contouring control scheme	119
6 Discussions	122
VII. CONCLUSION	123
1 Discussions and conclusions	123
2 Suggestions for future researches	125
REFERENCES 	126
PUBLICATION LIST 	136
VITA	138
                                    

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于少娟、齊向東和吳聚華，迭代學習控制理論及運用，第111-145頁，機械工業出版社，北京，2005。

謝勝利、田森平和謝振東，迭代學習控制的理論與運用，第206-281頁，科學出版社，北京，2005。

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