Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Industrial Control (AIC)
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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.
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Keywords
Table of contents (14 chapters)
Reviews
“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)
Authors and Affiliations
About the author
David Owens was elected a Fellow of the Royal Academy of Engineering for his contributions to knowledge in these and other areas.
Bibliographic Information
Book Title: Iterative Learning Control
Book Subtitle: An Optimization Paradigm
Authors: David H. Owens
Series Title: Advances in Industrial Control
DOI: https://doi.org/10.1007/978-1-4471-6772-3
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London 2016
Hardcover ISBN: 978-1-4471-6770-9Published: 10 November 2015
Softcover ISBN: 978-1-4471-6928-4Published: 23 August 2016
eBook ISBN: 978-1-4471-6772-3Published: 31 October 2015
Series ISSN: 1430-9491
Series E-ISSN: 2193-1577
Edition Number: 1
Number of Pages: XXVIII, 456
Topics: Control and Systems Theory, Systems Theory, Control, Artificial Intelligence, Machinery and Machine Elements, Robotics and Automation