Iterative Learning Control: An Optimization Paradigm

Author:   David H. Owens
Publisher:   Springer London Ltd
Edition:   1st ed. 2016
ISBN:  

9781447167709


Pages:   456
Publication Date:   10 November 2015
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Iterative Learning Control: An Optimization Paradigm


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Author:   David H. Owens
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   1st ed. 2016
Dimensions:   Width: 15.50cm , Height: 2.70cm , Length: 23.50cm
Weight:   8.454kg
ISBN:  

9781447167709


ISBN 10:   1447167708
Pages:   456
Publication Date:   10 November 2015
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Iterative Learning Control: Background and Review. Mathematical and Linear Modelling Methodologies.- Norm Optimal Iterative Learning Control: An Optimal Control Perspective.- Predicting the Effects of Non-minimum-phase Zeros.- Predictive Norm Optimal Iterative Learning Control.- Other Applications of Norm Optimal Iterative Learning Control.- Successive Projection Algorithms.- Parameter Optimal Iterative Learning Control.- Robustness of Parameter Optimal Iterative Learning Control.- Multi-parameter Optimal Iterative Learning Control.- No Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name: Table Normal ; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent: ; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family: Calibri , sans-serif ; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-fareast-language:EN-US;} nlinear Iterative Learning Control and Optimization.

Reviews

This book presents a comprehensive study of ILC from the optimization paradigm, more specifically, the NOILC optimization paradigm. The organization is clear, and the necessary fundamentals are self-contained. The mathematical analysis is rigorous, and the algorithms are detail complete. This book is suitable for academic researchers ... . It can be used a textbook for graduate students who are interested in ILC. It also gives rich motivations for researchers to study ILC from the optimization perspective. (Li Xia, IEEE Control Systems Magazine, Vol. 37 (2), April, 2017)


This book presents a comprehensive study of ILC from the optimization paradigm, more specifically, the NOILC optimization paradigm. The organization is clear, and the necessary fundamentals are self-contained. The mathematical analysis is rigorous, and the algorithms are detail complete. This book is suitable for academic researchers ... . It can be used a textbook for graduate students who are interested in ILC. It also gives rich motivations for researchers to study ILC from the optimization perspective. (Li Xia, IEEE Control Systems Magazine, Vol. 37 (2), April, 2017) This is a seminal text since it is very likely to generate interest and enthusiasm in the whole topic, and it provided a comprehensive overview of the mathematical methods that can be applied. It is very suitable for researchers in advanced control but it will also be useful to engineers involved with multi pass or repetitive processes where learning can provide a substantial improvement. (ACTC applied control technology consortium, actc-control.com, March, 2016)


Author Information

Professor Owens has 40 years of experience of Control Engineering theory and applications in areas including nuclear power, robotics and mechanical test. He has extensive teaching experience at both undergraduate and postgraduate levels in three UK universities. His research has included multivariable frequency domain theory and design, the theory of multivariable root loci, contributions to robust control theory, theoretical methods for controller design based on plant step data and involvement in aspects of adaptive control, model reduction and optimization-based design. His area of research that specifically underpins the text is his experience of modelling and analysis of systems with repetitive dynamics. Originally arising in control of underground coal cutters, my theory of multipass processes (developed in 1976 with follow-on applications introduced by J.B. Edwards) laid the foundation for analysis and design in this area and others including metal rolling and automated agriculture. This work led to substantial contributions (with collaborator E. Rogers and others) in the area of repetitive control systems (as part of 2D systems theory) but more specifically, since 1996, in the area of iterative learning control when I introduced the use of optimization to the ILC community in the form of norm optimal iterative learning control . Since that time he has continued to teach and research in areas related to this topic adding considerable detail and depth to the approach and introducing the ideas of parameter optimal iterative learning to simplify the implementations. This led to his development of a wide range of new algorithms, supporting analysis and applications to mechanical test. This work is also being applied to the development of data analysis tools for control in gantry robots and stroke rehabilitation equipment by collaborators at Southampton University. Work with S. Daley has also seen applications in automative test at Jaguar and related industrial sites. David Owens was elected a Fellow of the Royal Academy of Engineering for his contributions to knowledge in these and other areas.

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