Iterative Learning Control Algorithms and Experimental Benchmarking (E-Book) von Eric Rogers

Preface vii

1 Iterative Learning Control: Origins and General Overview1

1.1 The Origins of ILC2

1.2 A Synopsis of the Literature5

1.3 Linear Models and Control Structures6

1.3.1 Differential Linear Dynamics7

1.4 ILC for Time-Varying Linear Systems9

1.5 Discrete Linear Dynamics11

1.6 ILC in a 2D Linear Systems/Repetitive Processes Setting16

1.6.1 2D Discrete Linear Systems and ILC16

1.6.2 ILC in a Repetitive Process Setting17

1.7 ILC for Nonlinear Dynamics18

1.8 Robust, Stochastic, and Adaptive ILC19

1.9 Other ILC Problem Formulations21

1.10 Concluding Remarks22

2 Iterative Learning Control: Experimental Benchmarking23

2.1 Robotic Systems23

2.1.1 Gantry Robot23

2.1.2 Anthromorphic Robot Arm25

2.2 Electro-Mechanical Systems26

2.2.1 Nonminimum Phase System26

2.2.2 Multivariable Testbed29

2.2.3 Rack Feeder System30

2.3 Free Electron Laser Facility32

2.4 ILC in Healthcare37

2.5 Concluding Remarks38

3 An Overview of Analysis and Design for Performance39

3.1 ILC Stability and Convergence for Discrete Linear Dynamics39

3.1.1 Transient Learning41

3.1.2 Robustness42

3.2 Repetitive Process/2D Linear Systems Analysis43

3.2.1 Discrete Dynamics43

3.2.2 Repetitive Process Stability Theory46

3.2.3 Error Convergence Versus Along the Trial Performance51

3.3 Concluding Remarks55

4 Tuning and Frequency Domain Design of Simple Structure ILC Laws57

4.1 Tuning Guidelines57

4.2 Phase-Lead and Adjoint ILC Laws for Robotic-Assisted Stroke Rehabilitation58

4.2.1 Phase-Lead ILC61

4.2.2 Adjoint ILC63

4.2.3 Experimental Results63

4.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm68

4.3.1 Filtering74

4.3.2 Numerical Simulations75

4.3.3 Experimental Results75

4.4 Concluding Remarks81

5 Optimal ILC83

5.1 NOILC83

5.1.1 Theory83

5.1.2 NOILC Computation86

5.2 Experimental NOILC Performance89

5.2.1 Test Parameters90

5.3 NOILC Applied to Free Electron Lasers93

5.4 Parameter Optimal ILC96

5.4.1 An Extension to Adaptive ILC98

5.5 Predictive NOILC99

5.5.1 Controlled System Analysis104

5.5.2 Experimental Validation106

5.6 Concluding Remarks116

6 Robust ILC117

6.1 Robust Inverse Model-Based ILC117

6.2 Robust Gradient-Based ILC123

6.2.1 Model Uncertainty Case (i)127

6.2.2 Model Uncertainty Cases (ii) and (iii)128

6.3H Robust ILC132

6.3.1 Background and Early Results132

6.3.2H Based Robust ILC Synthesis137

6.3.3 A Design Example142

6.3.4 Robust ILC Analysis Revisited151

6.4 Concluding Remarks153

7 Repetitive Process-Based ILC Design155

7.1 Design with Experimental Validation155

7.1.1 Discrete Nominal Model Design155

7.1.2 Robust Design Norm-Bounded Uncertainty160

7.1.3 Robust Design Polytopic Uncertainty and Simplified Implementation165

7.1.4 Design for Differential Dynamics170

7.2 Repetitive Process-Based ILC Design Using Relaxed Stability Theory170

7.3 Finite Frequency Range Design and Experimental Validation178

7.3.1 Stability Analysis178

7.4 HOILC Design194

7.5 Inferential ILC Design196

7.6 Concluding Remarks202

8 Constrained ILC Design203

8.1 ILC with Saturating Inputs Design203

8.1.1 Observer-Based State Control Law Design203

8.1.2 ILC Design with Full State Feedback209

8.1.3 Comparison with an Alternative Design210

8.1.4 Experimental Results215

8.2 Constrained ILC Design for LTV Systems219

8.2.1 Problem Specification219

8.2.2 Implementation of Constrained Algorithm 1 a Receding Horizon Approach223

8.2.3 Constrained ILC Algorithm 3224

8.3 Experimental Validation on a High-Speed Rack Feeder System226

8.3.1 Simulation Case Studies226

8.3.2 Other Performance Issues230

8.3.3 Experimental Results236

8.3.4 Algorithm 1: QP-Based Constrained ILC236

8.3.5 Algorithm 2: Receding Horizon Approach-Based Constrained ILC237

8.4 Concluding Remarks238

9 ILC for Distributed Parameter Systems241

9.1 Gust Load Management for Wind Turbines241

9.1.1 Oscillatory Flow246

9.1.2 Flow with Vortical Disturbances251

9.1.3 Blade Conditioning Measures253

9.1.4 Actuator Dynamics and Trial-Varying ILC254

9.1.5 Proper Orthogonal Decomposition-Based Reduced Order Model Design257

9.2 Design Based on Finite-Dimensional Approximate Models with Experimental Validation266

9.3 Finite Element and Sequential Experimental Design-based ILC280

9.3.1 Finite Element Discretization281

9.3.2 Application of ILC283

9.3.3 Optimal Measurement Data Selection284

9.4 Concluding Remarks288

10 Nonlinear ILC289

10.1 Feedback Linearized ILC for Center-Articulated Industrial Vehicles289

10.2 InputOutput Linearization-based ILC Applied to Stroke Rehabilitation293

10.2.1 System Configuration and Modeling293

10.2.2 InputOutput Linearization296

10.2.3 Experimental Results299

10.3 Gap Metric ILC with Application to Stroke Rehabilitation302

10.4 Nonlinear ILC an Adaptive Lyapunov Approach310

10.4.1 Motivation and Background Results311

10.5 Extremum-Seeking ILC320

10.6 Concluding Remarks322

11 Newton Method Based ILC323

11.1 Background323

11.2 Algorithm Development324

11.2.1 Computation of Newton-Based ILC326

11.2.2 Convergence Analysis327

11.3 Monotonic Trial-to-Trial Error Convergence328

11.3.1 Monotonic Convergence with Parameter Optimization329

11.3.2 Parameter Optimization for Monotonic and Fast Trial-to-Trial Error Convergence330

11.4 Newton ILC for 3D Stroke Rehabilitation331

11.4.1 Experimental Results336

11.5 Constrained Newton ILC Design337

11.6 Concluding Remarks347

12 Stochastic ILC349

12.1 Background and Early Results349

12.2 Frequency Domain-Based Stochastic ILC Design356

12.3 Experimental Comparison of ILC Laws364

12.4 Repetitive Process-Based Analysis and Design378

12.5 Concluding Remarks387

13 Some Emerging Topics in Iterative Learning Control389

13.1 ILC for Spatial Path Tracking389

13.2 ILC in Agriculture and Food Production394

13.2.1 The Broiler Production Process395

13.2.2 ILC for FCR Minimization400

13.2.3 Design Validation404

13.3 ILC for Quantum Control406

13.4 ILC in the Utility Industries410

13.4.1 ILC Design413

13.5 Concluding Remarks415

Appendix A417

A.1 The Entries in the Transfer-Function Matrix (2.2)417

A.2 Entries in the Transfer-Function Matrix (2.4)418

A.3 MatricesE₁, E₂, H₁, andH₂ for the Designs of (7.36) and (7.37)419

References 421

Index 437

Iterative Learning Control Algorithms and Experimental Benchmarking

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. 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.

Professor Eric Rogers, Dr. Bing Chu, Professor Christopher Freeman, and Professor Paul Lewin, University of Southampton, UK