Data-driven feedforward learning using non-causal rational basis functions : application to an industrial flatbed printer: application to an industrial flatbed printer

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

4 Citaten (Scopus)

Samenvatting

Data-driven feedforward learning enables high performance for industrial motion systems based on measured data from previous motion tasks. The key aspect herein is the chosen feedforward parametrization, which should parsimoniously model the inverse system. At present, high performance comes at the cost of parametrizations that are nonlinear in the parameters and consequences thereof. A linear parametrization is proposed that enables parsimonious modeling of inverse systems for feedforward through the use of non-causal rational orthonormal basis functions. The benefits of the proposed parametrization are experimentally demonstrated on an industrial printer, including pre-actuation and cyclic pole repetition.

Originele taal-2Engels
Titel2018 Annual American Control Conference, ACC 2018
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's6672-6677
Aantal pagina's6
ISBN van geprinte versie9781538654286
DOI's
StatusGepubliceerd - 9 aug 2018
Evenement2018 Annual American Control Conference, (ACC2018) - Milwauke, Verenigde Staten van Amerika
Duur: 27 jun 201829 jun 2018
http://acc2018.a2c2.org/
http://acc2018.a2c2.org/

Congres

Congres2018 Annual American Control Conference, (ACC2018)
Verkorte titelACC2018
LandVerenigde Staten van Amerika
StadMilwauke
Periode27/06/1829/06/18
Internet adres

Vingerafdruk Duik in de onderzoeksthema's van 'Data-driven feedforward learning using non-causal rational basis functions : application to an industrial flatbed printer: application to an industrial flatbed printer'. Samen vormen ze een unieke vingerafdruk.

  • Citeer dit