Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems 2007 ed. [Kõva köide]

Part I Iterative Learning Control Overview
1 Introduction		3
1.1 General Overview of the Monograph		3
1.2 Iterative Learning Control		4
1.2.1 What Is Iterative Learning Control?		4
1.2.2 Classical ILC Update Law		7
1.2.3 The Periodicity and Repetitiveness in ILC		10
1.2.4 Advantages of Using ILC		11
1.3 Research Motivation		13
1.3.1 Motivation for Robust Interval Iterative Learning Control		13
1.3.2 Motivation for Hoc Iterative Learning Control		16
1.3.3 Motivation for Stochastic Iterative Learning Control		17
1.4 Original Contributions of the Monograph		17
2 An Overview of the ILC Literature		19
2.1 ILC Literature Search Methodology		20
2.2 Comments on the ILC Literature		20
2.3 IJC Special Issue		23
2.4 ILC-related Ph.D. Dissertations Since 1998		24
2.5 Chapter Summary		25
3 The Super-vector Approach		27
3.1 Asymptotic Stability of Higher-order SVILC		27
3.2 Monotonic Convergence of Higher-order SVILC		29
3.3 Chapter Summary		33
Part II Robust Interval Iterative Learning Control
4 Robust Interval Iterative Learning Control: Analysis		37
4.1 Interval Iterative Learning Control: Definitions		37
4.2 Robust Stability of Interval Iterative Learning Control		41
4.2.1 Asymptotical Stability of the Interval FOILC		41
4.2.2 Monotonic Convergence		43
4.2.3 Singular Value Approach		48
4.2.4 Robust Stability of Higher-order Interval Iterative Learning Control		48
4.3 Experimental Test		49
4.4 Chapter Summary		51
5 Schur Stability Radius of Interval Iterative Learning Control		55
5.1 Stability Radius		56
5.2 Optimization		62
5.3 Simulation Illustrations		63
5.3.1 Test. Setup		64
5.3.2 Test Results		64
5.4 Chapter Summary		68
6 Iterative Learning Control Design Based on Interval Model Conversion		69
6.1 Interval Model Conversion in ILC		69
6.2 Interval Matrix Eigenpair Bounds		71
6.3 Markov Parameter Bounds		75
6.4 Robust ILC Design		75
6.5 Simulation Illustrations		77
6.6 A Different Approach for Interval Model Conversion		79
6.7 Chapter Summary		80
Part III Iteration-domain Robustness
7 Robust Iterative Learning Control: Hoc, Approach		83
7.1 Introduct ion		83
7.2 Problem Formulation		84
7.2.1 A Generalized Framework		85
7.2.2 Iteration-domain Hinfinity Problem Formulation		86
7.3 Algebraic Hinfinity Approach to Iterative Learning Control		89
7.3.1 Iteration-varying Disturbances		89
7.3.2 Model Uncertainty		93
7.4 Simulation Illustrations		95
7.5 Chapter Summary		97
8 Robust Iterative Learning Control: Stochastic Approaches		101
8.1 Baseline Error Estimation in the Iteration Domain		102
8.1.1 Modeling		103
8.1.2 Analytical Solutions		106
8.1.3 Simulation Illustrations		111
8.1.4 Concluding Remarks		112
8.2 Iteration-varying Model Uncertainty		115
8.2.1 Basic Background Materials of Model Uncertain Super-vector ILC		116
8.2.2 ILC Design for Iteration-varying Model Uncertain System		117
8.2.3 Parameter Estimation		121
8.2.4 Simulation Illustrations		123
8.2.5 Concluding Remarks		123
8.3 Intermittent Iterative Learning Control		124
8.3.1 Intermittent ILC		125
8.3.2 Optimal Learning Gain Matrix Design for Intermittent ILC		127
8.3.3 Concluding Remarks		133
8.4 Chapter Summary		134
9 Conclusions		135
Appendices
A Taxonomy of Iterative Learning Control Literature		143
A.1 Taxonomy		143
A.2 Literature Related to ILC Applications		143
A.2.1 Robots		144
A.2.2 Rotary Systems		144
A.2.3 Batch/Factory/Chemical process		144
A.2.4 Bio/Artificial Muscle		145
A.2.5 Actuators		145
A.2.6 Semiconductor		145
A.2.7 Power Electronics		146
A.2.8 Miscellaneous		146
A.3 Literature Related to ILC Theories		146
A.3.1 General (Structure)		147
A.3.2 General (Update Rules)		147
A.3.3 Typical ILC Problems		148
A.3.4 Robustness Against Uncertainty, Time Varying, and/or Stochastic Noise		148
A.3.5 Optimal, Quadratic, and/or Optimization		149
A.3.6 Adaptive and/or Adaptive Approaches		149
A.3.7 Fuzzy or Neural Network ILC		149
A.3.8 ILC for Mechanical Nonlinearity Compensation		150
A.3.9 ILC for Other Repetitive Systems and Other Control Schemes		150
A.3.10 Miscellaneous		150
A.4 Discussion		152
B Maximum Singular Value of an Interval Matrix		153
B.1 Maximum Singular Value of a Square Interval Matrix		153
B.2 Maximum Singular Value of Non-square Interval Matrix		156
B.3 Illustrative Examples		157
B.3.1 Example 1: Non-square Case		157
B.3.2 Example 2: Square Case		157
B.4 Summary		159
C Robust Stability of Interval Polynomial Matrices		161
C.1 Interval Polynomial Matrices		161
C.2 Definitions and Preliminaries		162
C.3 Stability Condition for Interval Polynomial Matrices		163
C.3.1 The Stability of Polynomial Matrices: Part 1		164
C.3.2 The Stability of Polynomial Matrices: Part 2		165
C.3.3 The Stability of Interval Polynomial Matrices		168
C.4 Illustrative Examples		171
C.4.1 Example 1		171
C.4.2 Example 2		172
C.5 Summary		173
D Power of an Interval Matrix		175
D.1 Sensitivity Transfer Method		176
D.2 Illustrative Examples		178
D.2.1 Example 1		179
D.2.2 Example 2		180
D.3 Condition for Proposition D		181
D.4 Summary		186
References		187
Index		229

Hyo-Sung Ahn has research interests in the areas of robust iterative learning control, periodic adaptive learning control, networked control systems, neural networks, mobile robotics, navigation, biomechatronics, and aerospace engineering. He was research engineer in Space Development and Research Center, Korea Aerospace Indusstries LTD, Korea, and Upper Midwest Aerospace Consortium, USA. He received the M.S. degree from the University of North Dakota in Aerospace Engineering and the Ph.D. in Electrical Engineering from Utah State University. Dr. Ahn, with his co-authors, has been the primary developer of the ideas in the monograph and has a deep understanding of the design of iterative learning control systems, especially as regards robustness.



Professor Moore is the G.A. Dobelman Distinguished Chair and Professor of Engineering in the Division of Engineering at the Colorado School of Mines. He received the B.S. and M.S. degrees in electrical engineering from Louisiana State University and the University of Southern California, respectively. He received the Ph.D. in electrical engineering, with an emphasis in control theory, from Texas A&M University. Most recently he was a senior scientist at Johns Hopkins University's Applied Physics Laboratory, where he worked in the area of unattended air vehicles, cooperative control, and autonomous systems (2004-2005). He was previously an Associate Professor at Idaho State University (1989-1998) and a Professor of Electrical and Computer Engineering at Utah State University, where he was the Director of the Center for Self-Organizing and Intelligent Systems, directing multi-disciplinary research teams of students and professionals developing a variety of autonomous robots for government and commercial applications (1998 -2004). He also worked in industry for three years pre-Ph.D as a member of the technical staff at Hughes Aircraft Company. His general research interests include iterative learning control theory, autonomous systems and robotics, and applications of control to industrial and mechatronic systems. He is the author of the research monograph Iterative Learning Control for Deterministic Systems, published by Springer-Verlag in 1993, and co-author of the book Sensing, Modeling, and Control of Gas Metal Arc Welding, published by Elsevier in 2003. He is a professional engineer, involved in several professional societies and editorial activities, and is interested in engineering education pedagogy. Of particular relevance for the proposed monograph, Dr. Moore has been a seminal contributor and leader in the field of ILC. His early work in the field developed the idea of the supervector approach, and he has studied the problem of monotonic convergence, and he initiated the idea of studying robustness in the iteration domain. He has also been active in organizing ILC workshops, invited sessions on ILC at conferences, and editing special issues of journals. His insights on the ILC problem will directly influence the contents of the proposed monograph.



Dr YangQuan Chen is presently an assistant professor of Electrical and Computer Engineering Department and the Acting Director for CSOIS (Center for Self-Organizing and Intelligent Systems, www.csois.usu.edu) at Utah State University. He obtained his Ph.D. from Nanyang Technological University, Singapore in 1998, an MS from Beijing Institute of Technology (BIT) in 1989, and a BS from University of Science and Technology of Beijing (USTB) in 1985. Dr Chen has 12 US patents granted and 2 US patent applications published, most related to the implementation of ILC algorithms, which lends special insight into the ILC application examples found in the mongraph. He has published more than 200 academic papers and (co)authored more than 50 industrial reports. His recent books include Solving Advanced Applied Mathematical Problems Using Matlab (with Dingyu Xue, Tsinghua University Press. August 2004. 419 pages in Chinese. ISBN 7-302-09311-3/O.392), System Simulation Techniques with Matlab/Simulink (with Dingyu Xue, Tsinghua University Press, April 2002, ISBN7-302-05341-3/TP3137, in Chinese) and Iterative Learning Control: Convergence, Robustness and Applications (with Changyun Wen, Lecture Notes Series in Control and Information Science, Springer-Verlag, Nov. 1999, ISBN: 1-85233-190-9). His current research interests include autonomous navigation and intelligent control of a team of unmanned ground vehicles, machine vision for control and automation, distributed control systems (MAS-net: mobile actuator-sensor networks), fractional order control, interval computation, biofilm and chemotaxis modeling, nanomechatronics and biomechatronics, and iterative/repetitive/adaptive learning control. Dr Chen has been an Associate Editor in the Conference Editorial Board of IEEE Control Systems Society since 2002. He is a founding member of the ASME subcommittee of "Fractional Dynamics" in 2003. He is a senior member of IEEE, a member of ASME, and a member of ISIF (International Society for Information Fusion).