Feedforward motion control: from batch-to-batch learning to online parameter estimation

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

Feedforward control is essential in highperformance motion control. The aim of this paper is to develop a unified framework for automatic feedforward optimization from both batch-wise data sets as well as real-time data. A statistical analysis is employed to analyze the effect of noise, i.e., an iteration varying disturbance, on feedforward controller performance. This provides new insights, both potential advantages as well as possible hazards of real-time estimation are considered. Finally, a case study confirms and illustrates the results.

Originele taal-2Engels
Titel2019 American Control Conference, ACC 2019
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's947-952
Aantal pagina's6
ISBN van elektronische versie978-1-5386-7926-5
DOI's
StatusGepubliceerd - 1 jul 2019
Evenement2019 American Control Conference, ACC 2019 - Philadelphia, Verenigde Staten van Amerika
Duur: 10 jul 201912 jul 2019
http://acc2019.a2c2.org

Congres

Congres2019 American Control Conference, ACC 2019
Verkorte titelACC2019
LandVerenigde Staten van Amerika
StadPhiladelphia
Periode10/07/1912/07/19
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  • Citeer dit

    Mooren, N., Witvoet, G., & Oomen, T. (2019). Feedforward motion control: from batch-to-batch learning to online parameter estimation. In 2019 American Control Conference, ACC 2019 (blz. 947-952). [8814481] Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/acc.2019.8814481