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" Iterative learning control for systems with iteration-varying trial lengths : "
Dong Shen, Xuefang Li.
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BL
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| Record Number
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891128
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| Main Entry
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Shen, Dong
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| Title & Author
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Iterative learning control for systems with iteration-varying trial lengths : : synthesis and analysis /\ Dong Shen, Xuefang Li.
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| Publication Statement
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Singapore :: Springer,, [2019]
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| Page. NO
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1 online resource (xiv, 256 pages) :: illustrations (some color)
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| ISBN
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9789811361364
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: 9789811361371
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: 9811361363
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: 9811361371
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9789811361357
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9811361355
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Introduction -- Averaging and Expectation Techniques for Linear DT Systems -- Lifting Techniques for Linear DT Systems -- Moving Averaging Techniques for Linear DT Systems -- Switching System Techniques for Linear DT Systems -- Moving Averaging Tehcniques for Nonlinear CT Systems -- Modified Lambda-Norm Techniques for Nonlinear DT Systems -- Sampled-Data Control Techniques for Nonlinear CT Systems -- CEF Techniques for Parameterized Nonlinear CT Systems -- CEF Techniques for Nonparameterized Nonlinear CT Systems -- Summary and Future Research Directions -- Appendix.
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| Abstract
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This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.
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Algorithms.
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Automatic control.
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Engineering mathematics.
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System theory.
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Algorithms.
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Automatic control.
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Engineering mathematics.
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System theory.
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| Dewey Classification
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629.8
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| LC Classification
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TJ213.S54 2019
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| Added Entry
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Li, Xuefang
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