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Iterative Learning Control

Robustness and Monotonic Convergence for Interval Systems

Iterative Learning Control
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US$ 179.00
This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Two key problems with the fundamentals of iterative learning control (ILC) design as treated by existing work are: first, many ILC design strategies assume nominal knowledge of the system to be controlled and; second, it is well-known that many ILC algorithms do not produce monotonic convergence, though in applications monotonic convergence is often essential."Iterative Learning Control" takes account of the recently-developed comprehensive approach to robust ILC analysis and design established to handle the situation where the plant model is uncertain. Considering ILC in the iteration domain, it presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty.The topics include: use of a lifting technique to convert the two-dimensional ILC system, which has dynamics in both the time and iteration domains, into the supervector framework, which yields a one-dimensional system, with dynamics only in the iteration domain; development of iteration-domain uncertainty models in the supervector framework; ILC design for monotonic convergence when the plant is subject to parametric interval uncertainty in its Markov matrix; and, an algebraic H-infinity design methodology for ILC design when the plant is subject to iteration-domain frequency uncertainty.
Springer; June 2007
244 pages; ISBN 9781846288593
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