Iterative learning control for varying tasks: achieving optimality for rational basis functions

J. van Zundert, J.J. Bolder, T.A.E. Oomen

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

5 Citations (Scopus)
95 Downloads (Pure)

Abstract

Iterative Learning Control (ILC) can achieve superior tracking performance for systems that perform repeating tasks. However, the performance of standard ILC deteriorates dramatically when the task is varied. In this paper ILC is extended with rational basis functions to obtain excellent extrapolation properties. A new approach for rational basis functions is proposed where the iterative solution algorithm is of the form used in instrumental variable system identification algorithms. The optimal solution is expressed in terms of learning filters similar as in standard ILC. The proposed approach is shown to be superior over existing approaches in terms of performance by a simulation example.
Original languageEnglish
Title of host publication2015 American Control Conference (ACC 2015), July 1–3, 2015, Chicago, IL, USA
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3570-3575
Number of pages6
ISBN (Print)978-1-4799-8686-6
DOIs
Publication statusPublished - Jul 2015
Event2015 American Control Conference (ACC 2015), July 1–3, 2015, Chicago, IL, USA - Hilton Palmer House, Chicago, IL, United States
Duration: 1 Jul 20153 Jul 2015
http://acc2015.a2c2.org/

Conference

Conference2015 American Control Conference (ACC 2015), July 1–3, 2015, Chicago, IL, USA
Abbreviated titleACC 2015
CountryUnited States
CityChicago, IL
Period1/07/153/07/15
Internet address

Fingerprint Dive into the research topics of 'Iterative learning control for varying tasks: achieving optimality for rational basis functions'. Together they form a unique fingerprint.

Cite this