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Iterative learning control for uncertain systems: Noncausal finite time interval robust control design
Author(s) -
van de Wijdeven J. J. M.,
Donkers M. C. F.,
Bosgra O. H.
Publication year - 2010
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1657
Subject(s) - iterative learning control , control theory (sociology) , robust control , interval (graph theory) , convergence (economics) , robustness (evolution) , computer science , norm (philosophy) , mathematical optimization , quadratic equation , controller (irrigation) , control (management) , mathematics , control system , engineering , artificial intelligence , agronomy , biochemistry , chemistry , geometry , combinatorics , biology , economic growth , law , political science , electrical engineering , economics , gene
In this paper, we present a novel robust Iterative Learning Control (ILC) control strategy that is robust against model uncertainty as given by an additive uncertainty model. The design methodology hinges on ℋ ∞ optimization, but formulated such that the obtained ILC controller is not restricted to be causal, and inherently operates on a finite time interval. Optimization of the robust ILC (R‐ILC) solution is accomplished for the situation where any information about structure in the uncertainty is discarded, and for the situation where the information about the structure in the uncertainty is explicitly taken into account. Subsequently, the convergence and performance properties of resulting R‐ILC controlled system are analyzed. On an experimental set‐up, we show that the presented R‐ILC control strategy can outperform an existing linear‐quadratic norm‐optimal ILC approach and an existing causal R‐ILC approach based on frequency domain ℋ ∞ synthesis. Copyright © 2010 John Wiley & Sons, Ltd.