Robustness against model uncertainties of norm optimal iterative learning control

M.C.F. Donkers, J.J.M. Wijdeven, van de, O.H. Bosgra

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

32 Citations (Scopus)
3 Downloads (Pure)


In this paper, we study MIMO Iterative Learning Control (ILC) and its robustness against model uncertainty. Although it is argued that, so-called, norm optimal ILC controllers have some inherent robustness, not many results are available that can make quantitative statements about the allowable model uncertainty. In this paper, we derive sufficient conditions for robust convergence of the ILC algorithm in presence of an uncertain system with an additive uncertainty bound. These conditions are applied to norm optimal ILC, resulting in guidelines for robust controller design. Theoretical results are illustrated by simulations.
Original languageEnglish
Title of host publicationProceedings of the 2008 American Control Conference (ACC2008), Seattle, Washington, USA, June 11-13, 2008
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-424-42078-0
Publication statusPublished - 2008


Dive into the research topics of 'Robustness against model uncertainties of norm optimal iterative learning control'. Together they form a unique fingerprint.

Cite this