Advances in Industrial Control

Iterative Learning Control

An Optimization Paradigm

Authors: Owens, David H.

  • Low-order worked examples aid in the understanding of the basic principles and computational processes of algorithm design
  • Computational and application studies illustrate performance and help to identify the source of performance limitations
  • Provides researchers with a convenient source of open problems and suggested research directions for their solution
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eBook $109.00
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  • ISBN 978-1-4471-6772-3
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Hardcover $169.99
price for USA in USD
  • ISBN 978-1-4471-6770-9
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Softcover $139.99
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  • ISBN 978-1-4471-6928-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design.

Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities.

Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation.

Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.


About the authors

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.

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)


Table of contents (14 chapters)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-1-4471-6772-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.99
price for USA in USD
  • ISBN 978-1-4471-6770-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $139.99
price for USA in USD
  • ISBN 978-1-4471-6928-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Iterative Learning Control
Book Subtitle
An Optimization Paradigm
Authors
Series Title
Advances in Industrial Control
Copyright
2016
Publisher
Springer-Verlag London
Copyright Holder
Springer-Verlag London
eBook ISBN
978-1-4471-6772-3
DOI
10.1007/978-1-4471-6772-3
Hardcover ISBN
978-1-4471-6770-9
Softcover ISBN
978-1-4471-6928-4
Series ISSN
1430-9491
Edition Number
1
Number of Pages
XXVIII, 456
Topics