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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 ...
