DocumentCode :
2851569
Title :
Reduced-order ILC: The Internal Model Principle reconsidered
Author :
Pipeleers, G. ; Moore, K.L.
Author_Institution :
Dept. of Mech. Eng., Katholieke Univ. Leuven, Heverlee, Belgium
fYear :
2011
fDate :
June 29 2011-July 1 2011
Firstpage :
3639
Lastpage :
3644
Abstract :
When iterative learning control (ILC) is applied to improve a system´s tracking performance, the trial-invariant reference input is typically known or contained in a prescribed set of signals. Current ILC algorithms, however, neglect this information and only exploit the trial-invariance of the input signal. In this paper we propose a novel ILC design that explicitly incorporates the additional knowledge on the trial invariant input. The proposed design approach results in a reduced-order ILC, in the sense that the order of its trial domain description equals the number of given trial-invariant input signals that are to be tracked. In contrast, current ILC algorithms yield a trial-domain controller of order N, the ILC trial length in discrete time. We discuss the advantages and disadvantages of reduced-order ILC when it is designed to minimize a 2-norm based objective.
Keywords :
adaptive control; control system synthesis; discrete time systems; iterative methods; learning systems; reduced order systems; signal processing; ILC trial length; discrete time; internal model principle; iterative learning control; reduced-order ILC algorithms; trial-domain controller; trial-invariant input signals; trial-invariant reference input; Asymptotic stability; Mathematical model; Poles and zeros; Sensitivity; Stability analysis; Time domain analysis; Tracking loops;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2011
Conference_Location :
San Francisco, CA
ISSN :
0743-1619
Print_ISBN :
978-1-4577-0080-4
Type :
conf
DOI :
10.1109/ACC.2011.5991067
Filename :
5991067
Link To Document :
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