Iterative Learning Control Algorithms and Experimental Benchmarking

Informationen zum Autor Professor Eric Rogers, Dr. Bing Chu, Professor Christopher Freeman, and Professor Paul Lewin, University of Southampton, UK Klappentext Iterative Learning CONTROL ALGORITHMS AND EXPERIMENTAL BENCHMARKINGIterative Learning Control Algorithms and Experimental BenchmarkingPresents key cutting edge research into the use of iterative learning controlThe book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. The book provides integrated coverage of the major approaches to-date in terms of basic systems, theoretic properties, design algorithms, and experimentally measured performance, as well as the links with repetitive control and other related areas.Key features:* Provides comprehensive coverage of the main approaches to ILC and their relative advantages and disadvantages.* Presents the leading research in the field along with experimental benchmarking results.* Demonstrates how this approach can extend out from engineering to other areas and, in particular, new research into its use in healthcare systems/rehabilitation robotics.The book is essential reading for researchers and graduate students in iterative learning control, repetitive control and, more generally, control systems theory and its applications. Zusammenfassung Presents key cutting edge research into the use of iterative learning control The book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. Inhaltsverzeichnis Preface vii 1 Iterative Learning Control: Origins and General Overview  1 1.1 The Origins of ILC  2 1.2 A Synopsis of the Literature  5 1.3 Linear Models and Control Structures  6 1.3.1 Differential Linear Dynamics  7 1.4 ILC for Time-Varying Linear Systems  9 1.5 Discrete Linear Dynamics  11 1.6 ILC in a 2D Linear Systems/Repetitive Processes Setting  16 1.6.1 2D Discrete Linear Systems and ILC  16 1.6.2 ILC in a Repetitive Process Setting  17 1.7 ILC for Nonlinear Dynamics  18 1.8 Robust, Stochastic, and Adaptive ILC  19 1.9 Other ILC Problem Formulations  21 1.10 Concluding Remarks  22 2 Iterative Learning Control: Experimental Benchmarking  23 2.1 Robotic Systems  23 2.1.1 Gantry Robot  23 2.1.2 Anthromorphic Robot Arm  25 2.2 Electro-Mechanical Systems  26 2.2.1 Nonminimum Phase System  26 2.2.2 Multivariable Testbed  29 2.2.3 Rack Feeder System  30 2.3 Free Electron Laser Facility  32 2.4 ILC in Healthcare  37 2.5 Concluding Remarks  38 3 An Overview of Analysis and Design for Performance  39 3.1 ILC Stability and Convergence for Discrete Linear Dynamics  39 3.1.1 Transient Learning  41 3.1.2 Robustness  42 3.2 Repetitive Process/2D Linear Systems Analysis  43 3.2.1 Discrete Dynamics  43 3.2.2 Repetitive Process Stability Theory  46 3.2.3 Error Convergence Versus Along the Trial Performance  51 3.3 Concluding Remarks  55 4 Tuning and Frequency Domain Design of Simple Structure ILC Laws  57 4.1 Tuning Guidelines  57 4.2 Phase-Lead and Adjoint ILC Laws for Robotic-Assisted Stroke Rehabilitation  58 4.2.1 Phase-Lead ILC  61 4.2.2 Adjoint ILC  63 4.2.3 Experimental Results  63 4.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm  68 4.3.1 Filtering  74 ...

Sommario

Preface vii
 
1 Iterative Learning Control: Origins and General Overview 1
 
1.1 The Origins of ILC 2
 
1.2 A Synopsis of the Literature 5
 
1.3 Linear Models and Control Structures 6
 
1.3.1 Differential Linear Dynamics 7
 
1.4 ILC for Time-Varying Linear Systems 9
 
1.5 Discrete Linear Dynamics 11
 
1.6 ILC in a 2D Linear Systems/Repetitive Processes Setting 16
 
1.6.1 2D Discrete Linear Systems and ILC 16
 
1.6.2 ILC in a Repetitive Process Setting 17
 
1.7 ILC for Nonlinear Dynamics 18
 
1.8 Robust, Stochastic, and Adaptive ILC 19
 
1.9 Other ILC Problem Formulations 21
 
1.10 Concluding Remarks 22
 
2 Iterative Learning Control: Experimental Benchmarking 23
 
2.1 Robotic Systems 23
 
2.1.1 Gantry Robot 23
 
2.1.2 Anthromorphic Robot Arm 25
 
2.2 Electro-Mechanical Systems 26
 
2.2.1 Nonminimum Phase System 26
 
2.2.2 Multivariable Testbed 29
 
2.2.3 Rack Feeder System 30
 
2.3 Free Electron Laser Facility 32
 
2.4 ILC in Healthcare 37
 
2.5 Concluding Remarks 38
 
3 An Overview of Analysis and Design for Performance 39
 
3.1 ILC Stability and Convergence for Discrete Linear Dynamics 39
 
3.1.1 Transient Learning 41
 
3.1.2 Robustness 42
 
3.2 Repetitive Process/2D Linear Systems Analysis 43
 
3.2.1 Discrete Dynamics 43
 
3.2.2 Repetitive Process Stability Theory 46
 
3.2.3 Error Convergence Versus Along the Trial Performance 51
 
3.3 Concluding Remarks 55
 
4 Tuning and Frequency Domain Design of Simple Structure ILC Laws 57
 
4.1 Tuning Guidelines 57
 
4.2 Phase-Lead and Adjoint ILC Laws for Robotic-Assisted Stroke Rehabilitation 58
 
4.2.1 Phase-Lead ILC 61
 
4.2.2 Adjoint ILC 63
 
4.2.3 Experimental Results 63
 
4.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm 68
 
4.3.1 Filtering 74
 
4.3.2 Numerical Simulations 75
 
4.3.3 Experimental Results 75
 
4.4 Concluding Remarks 81
 
5 Optimal ILC 83
 
5.1 NOILC 83
 
5.1.1 Theory 83
 
5.1.2 NOILC Computation 86
 
5.2 Experimental NOILC Performance 89
 
5.2.1 Test Parameters 90
 
5.3 NOILC Applied to Free Electron Lasers 93
 
5.4 Parameter Optimal ILC 96
 
5.4.1 An Extension to Adaptive ILC 98
 
5.5 Predictive NOILC 99
 
5.5.1 Controlled System Analysis 104
 
5.5.2 Experimental Validation 106
 
5.6 Concluding Remarks 116
 
6 Robust ILC 117
 
6.1 Robust Inverse Model-Based ILC 117
 
6.2 Robust Gradient-Based ILC 123
 
6.2.1 Model Uncertainty -Case (i) 127
 
6.2.2 Model Uncertainty -Cases (ii) and (iii) 128
 
6.3 H infinity Robust ILC 132
 
6.3.1 Background and Early Results 132
 
6.3.2 H infinity Based Robust ILC Synthesis 137
 
6.3.3 A Design Example 142
 
6.3.4 Robust ILC Analysis Revisited 151
 
6.4 Concluding Remarks 153
 
7 Repetitive Process-Based ILC Design 155
 
7.1 Design with Experimental Validation 155
 
7.1.1 Discrete Nominal Model Design 155
 
7.1.2 Robust Design -Norm-Bounded Uncertainty 160
 
7.1.3 Robust Design - Polytopic Uncertainty and Simplified Implementation 165
 
7.1.4 Design for Differential Dynamics 170
 
7.2 Repetitive Process-Based ILC Design Using Relaxed Stability Theory 170
 
7.3 Finite Frequency Range Design and Experimental Validation 178
 
7.3.1 Stability Analysis 178
 
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Dettagli sul prodotto

• Autori: Bing Chu, Christopher Freeman, Christopher et al Freeman, Paul Lewin, Eric Rogers, Eric (University of Southampton Rogers, Rogers Eric, ROGERS ERIC OWENS DAVID H LEWIN
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 16.02.2023
• EAN: 9780470745045
• ISBN: 978-0-470-74504-5
• Pagine: 448
• Categorie: Scienze naturali, medicina, informatica, tecnica > Tecnica > Meccanica, tecnica di produzione; Maschinenbau, Regelungstechnik, Iteration, Mechanical Engineering, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Mess- u. Regeltechnik, Control Process & Measurements, Control Systems Technology