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E-raamat: Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation

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Practical Iterative Learning Control with Frequency Domain Design and Sampled Data ImplementationSuurem pilt
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This book examines iterative learning control (ILC) with a focus on design and implementation. It presents a framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between performance and stability.

This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much higher accuracy than a feedback control alone can offer. With the proposed ILC algorithms, it is possible that machines can work to their hardware design limits set by sensors and actuators. The target audience for this book includes scientists, engineers and practitioners involved in any systems with repetitive operations.
1 Introduction 1(24)
1.1 Background
1(2)
1.1.1 What Is ILC9
1(1)
1.1.2 A Brief History
2(1)
1.2 Basics of ILC
3(2)
1.2.1 ILC Formulation
3(1)
1.2.2 Comparison of ILC in Different Domains
4(1)
1.3 ILC Design and Analysis
5(11)
1.3.1 ILC Learning Laws
5(2)
1.3.2 Two ILC Configurations
7(2)
1.3.3 Convergence Analysis
9(3)
1.3.4 Transient Analysis
12(4)
1.4 Robotic System with ILC
16(2)
1.5 About the Book
18(1)
References
19(6)
2 Learnable Band Extension and Multi-channel Configuration 25(28)
2.1 A-Type Learning Control
26(1)
2.2 Convergence Analysis of A-Type ILC
26(1)
2.3 Design of A-Type ILC
27(2)
2.3.1 Lead-Time Selection
28(1)
2.3.2 Gain Selection
28(1)
2.3.3 Robustness in Design
28(1)
2.4 A Design Example of A-Type ILC
29(4)
2.4.1 Learning Control Design
29(2)
2.4.2 Comparison of D-, P-, PD-, and A-Type ILCs
31(1)
2.4.3 Case Study and Experiments
32(1)
2.5 A-Type ILC Based Multiple Channel Learning
33(8)
2.5.1 Multi-channel Structure for ILC
35(3)
2.5.2 Error Separation
38(3)
2.6 Multi-channel A-Type ILC
41(1)
2.7 Design of Multi-channel A-Type ILC
42(2)
2.8 Robot Application of Multi-channel A-Type ILCs
44(5)
2.9 Conclusion
49(1)
References
50(3)
3 Learnable Bandwidth Extension by Auto-Tunings 53(22)
3.1 Cutoff Frequency Tuning
54(9)
3.1.1 Objective and Problems
54(1)
3.1.2 Learning Stability
55(2)
3.1.3 Learning Divergence
57(3)
3.1.4 Cutoff Frequency Tuning
60(1)
3.1.5 Termination of Tuning
61(2)
3.2 Lead Step Tuning
63(3)
3.2.1 Basis of Tuning
64(1)
3.2.2 Tuning Method
64(2)
3.3 Experiment on Auto-Tuning ILC
66(7)
3.3.1 Experiment 1: A-Type ILC with 1 = 5 and γ = 1
67(2)
3.3.2 Experiment 2: One-Step-Ahead ILC with 1 = 1 and γ = 1
69(2)
3.3.3 Experiment 3: Tuning Lead Step with γ = 1
71(2)
3.4 Conclusion
73(1)
References
73(2)
4 Reverse Time Filtering Based ILC 75(28)
4.1 Best Phase Lead and Generation Method for SISO ILC System
76(3)
4.2 Learning Control Using Reversed Time Input Runs
79(2)
4.2.1 Learning Law
79(1)
4.2.2 Model Based Approach
80(1)
4.3 Comparison with Other Works
81(1)
4.4 Case Study of Robot Application
82(8)
4.4.1 Exact Zero Phase
82(2)
4.4.2 Reverse Time Filtering Using a Model
84(1)
4.4.3 Robot Performance and Experiments
85(5)
4.5 MIMO ILC System and Error Contraction
90(1)
4.6 Clean System Inversion ILC
91(3)
4.7 System Hermitian ILC
94(2)
4.8 An Example of Robot Joints and Experiments
96(4)
4.9 Conclusion
100(1)
References
101(2)
5 Wavelet Transform Based Frequency Tuning ILC 103(24)
5.1 Wavelet Packet Algorithm for Error Analysis
104(5)
5.1.1 Wavelet Packet Algorithm
105(2)
5.1.2 Error Analysis Using Wavelet Packet Algorithm
107(2)
5.2 Cutoff Frequency Tuning ILC
109(4)
5.2.1 Cutoff Frequency Tuning Scheme
111(1)
5.2.2 Design of Zero-Phase Low-Pass Filter
112(1)
5.3 Time-Frequency Domain Analysis
113(2)
5.4 Case Study of Frequency Tuning ILC
115(10)
5.4.1 Determination of Learning Gain
115(2)
5.4.2 Determination of Lead Step
117(1)
5.4.3 Determination of Decomposition Level
118(1)
5.4.4 Experimental Results
119(6)
5.5 Conclusion
125(1)
References
125(2)
6 Learning Transient Performance with Cutoff-Frequency Phase-In 127(26)
6.1 Upper Bound of Trajectory Length for Good Learning Transient
128(5)
6.2 Cutoff-Frequency Phase-In Method
133(1)
6.3 Sliding Cutoff-Frequency Phase-In Method
134(1)
6.4 Robot Case Study with Experimental Results
135(16)
6.4.1 Parameter Selection
135(1)
6.4.2 Overcoming Initial Position Offset
136(5)
6.4.3 Improving Tracking Accuracy
141(10)
6.5 Conclusion
151(1)
References
151(2)
7 Pseudo-Downsampled ILC 153(28)
7.1 Downsampled Learning
153(9)
7.1.1 Pseudo-Downsampled ILC
158(2)
7.1.2 Two-Mode ILC
160(2)
7.2 Learning Data Processing
162(5)
7.2.1 Signal Extension
162(1)
7.2.2 Anti-aliasing Filtering and Anti-imaging Filtering
163(1)
7.2.3 Simulation Results
164(3)
7.3 Convergence Analysis
167(5)
7.3.1 Convergence of Pseudo-Downsampled ILC
167(5)
7.3.2 Convergence Analysis of Two-Mode ILC
172(1)
7.4 Experimental Study of Downsampled ILC
172(7)
7.4.1 Parameter Selection
172(2)
7.4.2 Experimental Study of Two-Mode ILC
174(5)
7.5 Conclusion
179(1)
References
179(2)
8 Cyclic Pseudo-Downsampled ILC 181(30)
8.1 Cyclic Pseudo-Downsampling ILC
182(1)
8.2 Convergence and Robustness Analysis
183(11)
8.3 Robot Application
194(14)
8.3.1 Parameter Selection
194(2)
8.3.2 Experiment of Cyclic Pseudo-Downsampled ILC
196(12)
8.4 Conclusion
208(1)
References
209(2)
9 Possible Future Research 211(4)
Appendix A: A Robotic Test-Bed for Iterative Learning Control 215
Dr. Danwei Wang received his Ph.D and MSE degrees from the University of Michigan, Ann Arbor in 1989 and 1984, respectively. He received his B.E degree from the South China University of Technology, China in 1982. Now, he is a professor in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He served as head of Division of Control and Instrumentation from 2005 to 2011. He has served as general chairman, technical chairman and various positions in international conferences. He is an associate editor of International Journal of Humanoid Robotics and invited guest editor of various international journals. He was a recipient of Alexander von Humboldt fellowship, Germany. He has published widely in the areas of iterative learning control, repetitive control, fault diagnosis and failure prognosis, satellite formation dynamics and control, as well as manipulator/mobile robot dynamics, path planning, and control.

Dr. Yongqiang Ye received the B.E. and MEng degrees in electrical engineering from Zhejiang University, China, in 1994 and 1997, respectively, and the Ph.D. degree in electrical engineering from Nanyang Technological University, Singapore, in 2004. From 2006, he had been a Postdoctoral Research Fellow for 3 years in Canada with Lakehead University, Carleton University, and Dalhousie University, respectively. Since 2009, he has been a Professor with the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. He has authored or coauthored more than 24 international journal papers. His research interests include iterative learning control and repetitive control, power electronics control, and image processing. He is a senior member of IEEE.

Dr. Bin Zhang received the B.E. and M.E. degrees from Nanjing University of Science and Technology, Nanjing, China, in 1993 and 1999, respectively,  both in Mechanical Engineering, and the Ph.D. degree in ElectricalEngineering from Nanyang Technological University, Singapore, in 2005. After his graduation, he joined the School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA as a post-doc research fellow. In 2009, he joined Impact Technologies, LLC, Rochester, NY as a senior project engineer and later lead engineer. Then, he joined the R&D of General Motors, Detroit, MI as a senior researcher in 2011. Since 2012, he has been an Assistant Professor in the Department of Electrical Engineering at the University of South Carolina, Columbia, SC.  He has about 15 years experience in dynamics and control, intelligent systems, mechatronics, prognostics and health management. He has authored and co-authored more than 80 papers in areas of his expertise. He is currently an Associate Editor for IEEE Transactions on Industrial Electronics, International Journal of Fuzzy Logic and Intelligent Systems. He is a senior member of IEEE.
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