Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

4 Citaten (Scopus)
100 Downloads (Pure)

Samenvatting

Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer.
Originele taal-2Engels
TitelProceedings of the 2015 American Control Conference (ACC 2015), 1-3 july 2015, Chicago, United States
Plaats van productieChicago
UitgeverijACC
Pagina's3546-3551
ISBN van geprinte versie978-1-4799-8686-6
StatusGepubliceerd - 2015

Vingerafdruk Duik in de onderzoeksthema's van 'Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design'. Samen vormen ze een unieke vingerafdruk.

  • Citeer dit

    Bolder, J. J., & Oomen, T. A. E. (2015). Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design. In Proceedings of the 2015 American Control Conference (ACC 2015), 1-3 july 2015, Chicago, United States (blz. 3546-3551). ACC.