Control-relevant neural networks for intelligent motion feedforward

Leontine Aarnoudse, Wataru Ohnishi, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

11 Citaten (Scopus)
3 Downloads (Pure)

Samenvatting

Neural networks have large potential for motion feedforward because of their ability to approximate a wide range of functions. The aim of this paper is to develop a systematic framework for application of neural networks to motion feedforward, that leads to an intelligent motion feedforward approach in the sense that it achieves both flexibility for varying references and high performance. Iterative learning control is used to generate training data, and a control-relevant performance function is introduced. Non-causal feedforward is enabled through two network configurations that enable respectively finite and infinite preview. The approach is experimentally validated on an industrial flatbed printer.

Originele taal-2Engels
Titel2021 IEEE International Conference on Mechatronics (ICM)
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's6
ISBN van elektronische versie9781728144429
DOI's
StatusGepubliceerd - 30 mrt. 2021
Evenement2021 IEEE International Conference on Mechatronics, ICM 2021 - Kashiwa, Japan
Duur: 7 mrt. 20219 mrt. 2021

Congres

Congres2021 IEEE International Conference on Mechatronics, ICM 2021
Land/RegioJapan
StadKashiwa
Periode7/03/219/03/21

Bibliografische nota

Publisher Copyright:
© 2021 IEEE.

Financiering

This work is part of the research programme VIDI with project number 15698, which is (partly) financed by NWO.

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