Iterative Learning Control: An Optimization Paradigm

Iterative Learning Control: An Optimization Paradigm

by David H. Owens

Paperback(Softcover reprint of the original 1st ed. 2016)

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Product Details

ISBN-13: 9781447169284
Publisher: Springer London
Publication date: 08/23/2016
Series: Advances in Industrial Control
Edition description: Softcover reprint of the original 1st ed. 2016
Pages: 456
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

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.

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.

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