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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Produktdetaljer
ISBN
9781447167723
Publisert
2018
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter