Iterative Learning Control Algorithms and Experimental Benchmarking

ISBN-13: 9780470745045 / Angielski / Twarda / 2023 / 400 str.

Eric Rogers;David H. Owens;Paul Lewin

Opis

With Iterative Learning Control Algorithms and Experimental Benchmarking the authors discuss the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. They provide an integrated coverage of the major approaches to-date in terms of basic systems theoretic properties, design algorithms, and experimentally measured performance as well the links with repetitive control and other related areas. A large part of the experimental verification comes from a joint research programme co-directed by the authors whose deliverables to-date include the design, commissioning and use of testbed facilities on which ILC and repetitive control algorithms can be experimentally compared. The authors present the results of this research, which has led to the development of the application of ILC in robotic systems for rehabilitation systems for stroke patients.

• Provides comprehensive coverage of the main approaches to iterative learning control (ILC) and their relative advantages and disadvantages
• Presents the leading research in the field along with a unique experimental benchmarking system
• 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

Spis treści

Preface vii1 Iterative Learning Control: Origins and General Overview 11.1 The Origins of ILC 21.2 A Synopsis of the Literature 51.3 Linear Models and Control Structures 61.3.1 Differential Linear Dynamics 71.4 ILC for Time-Varying Linear Systems 91.5 Discrete Linear Dynamics 111.6 ILC in a 2D Linear Systems/Repetitive Processes Setting 161.6.1 2D Discrete Linear Systems and ILC 161.6.2 ILC in a Repetitive Process Setting 171.7 ILC for Nonlinear Dynamics 181.8 Robust, Stochastic, and Adaptive ILC 191.9 Other ILC Problem Formulations 211.10 Concluding Remarks 222 Iterative Learning Control: Experimental Benchmarking 232.1 Robotic Systems 232.1.1 Gantry Robot 232.1.2 Anthromorphic Robot Arm 252.2 Electro-Mechanical Systems 262.2.1 Nonminimum Phase System 262.2.2 Multivariable Testbed 292.2.3 Rack Feeder System 302.3 Free Electron Laser Facility 322.4 ILC in Healthcare 372.5 Concluding Remarks 383 An Overview of Analysis and Design for Performance 393.1 ILC Stability and Convergence for Discrete Linear Dynamics 393.1.1 Transient Learning 413.1.2 Robustness 423.2 Repetitive Process/2D Linear Systems Analysis 433.2.1 Discrete Dynamics 433.2.2 Repetitive Process Stability Theory 463.2.3 Error Convergence Versus Along the Trial Performance 513.3 Concluding Remarks 554 Tuning and Frequency Domain Design of Simple Structure ILC Laws 574.1 Tuning Guidelines 574.2 Phase-Lead and Adjoint ILC Laws for Robotic-Assisted Stroke Rehabilitation 584.2.1 Phase-Lead ILC 614.2.2 Adjoint ILC 634.2.3 Experimental Results 634.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm 684.3.1 Filtering 744.3.2 Numerical Simulations 754.3.3 Experimental Results 754.4 Concluding Remarks 815 Optimal ILC 835.1 NOILC 835.1.1 Theory 835.1.2 NOILC Computation 865.2 Experimental NOILC Performance 895.2.1 Test Parameters 905.3 NOILC Applied to Free Electron Lasers 935.4 Parameter Optimal ILC 965.4.1 An Extension to Adaptive ILC 985.5 Predictive NOILC 995.5.1 Controlled System Analysis 1045.5.2 Experimental Validation 1065.6 Concluding Remarks 1166 Robust ILC 1176.1 Robust Inverse Model-Based ILC 1176.2 Robust Gradient-Based ILC 1236.2.1 Model Uncertainty -Case (i) 1276.2.2 Model Uncertainty -Cases (ii) and (iii) 1286.3 H infinity Robust ILC 1326.3.1 Background and Early Results 1326.3.2 H infinity Based Robust ILC Synthesis 1376.3.3 A Design Example 1426.3.4 Robust ILC Analysis Revisited 1516.4 Concluding Remarks 1537 Repetitive Process-Based ILC Design 1557.1 Design with Experimental Validation 1557.1.1 Discrete Nominal Model Design 1557.1.2 Robust Design -Norm-Bounded Uncertainty 1607.1.3 Robust Design - Polytopic Uncertainty and Simplified Implementation 1657.1.4 Design for Differential Dynamics 1707.2 Repetitive Process-Based ILC Design Using Relaxed Stability Theory 1707.3 Finite Frequency Range Design and Experimental Validation 1787.3.1 Stability Analysis 1787.4 HOILC Design 1947.5 Inferential ILC Design 1967.6 Concluding Remarks 2028 Constrained ILC Design 2038.1 ILC with Saturating Inputs Design 2038.1.1 Observer-Based State Control Law Design 2038.1.2 ILC Design with Full State Feedback 2098.1.3 Comparison with an Alternative Design 2108.1.4 Experimental Results 2158.2 Constrained ILC Design for LTV Systems 2198.2.1 Problem Specification 2198.2.2 Implementation of Constrained Algorithm 1 - a Receding Horizon Approach 2238.2.3 Constrained ILC Algorithm 3 2248.3 Experimental Validation on a High-Speed Rack Feeder System 2268.3.1 Simulation Case Studies 2268.3.2 Other Performance Issues 2308.3.3 Experimental Results 2368.3.4 Algorithm 1: QP-Based Constrained ILC 2368.3.5 Algorithm 2: Receding Horizon Approach-Based Constrained ILC 2378.4 Concluding Remarks 2389 ILC for Distributed Parameter Systems 2419.1 Gust Load Management for Wind Turbines 2419.1.1 Oscillatory Flow 2469.1.2 Flow with Vortical Disturbances 2519.1.3 Blade Conditioning Measures 2539.1.4 Actuator Dynamics and Trial-Varying ILC 2549.1.5 Proper Orthogonal Decomposition-Based Reduced Order Model Design 2579.2 Design Based on Finite-Dimensional Approximate Models with Experimental Validation 2669.3 Finite Element and Sequential Experimental Design-based ILC 2809.3.1 Finite Element Discretization 2819.3.2 Application of ILC 2839.3.3 Optimal Measurement Data Selection 2849.4 Concluding Remarks 28810 Nonlinear ILC 28910.1 Feedback Linearized ILC for Center-Articulated Industrial Vehicles 28910.2 Input-Output Linearization-based ILC Applied to Stroke Rehabilitation 29310.2.1 System Configuration and Modeling 29310.2.2 Input-Output Linearization 29610.2.3 Experimental Results 29910.3 Gap Metric ILC with Application to Stroke Rehabilitation 30210.4 Nonlinear ILC - an Adaptive Lyapunov Approach 31010.4.1 Motivation and Background Results 31110.5 Extremum-Seeking ILC 32010.6 Concluding Remarks 32211 Newton Method Based ILC 32311.1 Background 32311.2 Algorithm Development 32411.2.1 Computation of Newton-Based ILC 32611.2.2 Convergence Analysis 32711.3 Monotonic Trial-to-Trial Error Convergence 32811.3.1 Monotonic Convergence with Parameter Optimization 32911.3.2 Parameter Optimization for Monotonic and Fast Trial-to-Trial Error Convergence 33011.4 Newton ILC for 3D Stroke Rehabilitation 33111.4.1 Experimental Results 33611.5 Constrained Newton ILC Design 33711.6 Concluding Remarks 34712 Stochastic ILC 34912.1 Background and Early Results 34912.2 Frequency Domain-Based Stochastic ILC Design 35612.3 Experimental Comparison of ILC Laws 36412.4 Repetitive Process-Based Analysis and Design 37812.5 Concluding Remarks 38713 Some Emerging Topics in Iterative Learning Control 38913.1 ILC for Spatial Path Tracking 38913.2 ILC in Agriculture and Food Production 39413.2.1 The Broiler Production Process 39513.2.2 ILC for FCR Minimization 40013.2.3 Design Validation 40413.3 ILC for Quantum Control 40613.4 ILC in the Utility Industries 41013.4.1 ILC Design 41313.5 Concluding Remarks 415Appendix A 417A.1 The Entries in the Transfer-Function Matrix (2.2) 417A.2 Entries in the Transfer-Function Matrix (2.4) 418A.3 Matrices E1, E2, H1, and H2 for the Designs of (7.36) and (7.37) 419References 421Index 437

O autorze

Eric Rogers is Professor of Control Systems Theory and Design in the School of Electronics and Computer Science at Southampton University.  He teaches control and systems, circuit analysis and signal processing, and his research interests include feedback and stability theory for linear repetitive processes, iterative learning control theory and experimental verification, and iterative learning/repetitive Control duality. He is Editor–in–Chief of The International Journal of Control and Editor of the Taylor and Francis Research Text Series on Systems and Control. He has authored 3 research monographs and edited 1 research text.

Professor David Owens has been the Head of the Department of Automatic Control and Systems Engineering at Southampton University since 1999 and was Dean of Engineering at Sheffield University in the period 2002–2006. His research interests include control systems design, systems modelling; adaptive and robust control, repetitive control, iterative learning systems, nonlinear control systems and control systems applications. He is Joint Editor–in–chief of the IMA International Journal of Mathematical Control and Information.

Dr Paul Lewin teaches high–voltage engineering and electrical drives within the School of Electronics and Computer Science at Southampton University.