Low-order system identification and optimal control of intersample behavior in ILC

T.A.E. Oomen, J.J.M. Wijdeven, van de, O.H. Bosgra

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

1 Citation (Scopus)


Iterative learning control (ILC) enables high tracking performance of batch repetitive processes. Common ILC approaches resort to discrete time system representations and hence are not able to guarantee good intersample behavior in case the underlying system evolves in continuous time. The aim of this paper is to explicitly deal with the intersample behavior in ILC. A multirate, parametric, and low-order approach to both identification for ILC and subsequent optimal ILC is presented that results in a low computational burden. The approach appropriately deals with the time-varying nature of multirate systems. The proposed multirate identification and ILC algorithms are shown to outperform common ILC approaches in a simulation example.
Original languageEnglish
Title of host publicationProceedings of the 28th American Control Conference, (ACC '09) 10 - 12 June 2009, St. Louis, MO, USA
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4244-4523-3
Publication statusPublished - 2009


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