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-2 | Engels |
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Titel | 2021 IEEE International Conference on Mechatronics (ICM) |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Aantal pagina's | 6 |
ISBN van elektronische versie | 9781728144429 |
DOI's | |
Status | Gepubliceerd - 30 mrt. 2021 |
Evenement | 2021 IEEE International Conference on Mechatronics, ICM 2021 - Kashiwa, Japan Duur: 7 mrt. 2021 → 9 mrt. 2021 |
Congres
Congres | 2021 IEEE International Conference on Mechatronics, ICM 2021 |
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Land/Regio | Japan |
Stad | Kashiwa |
Periode | 7/03/21 → 9/03/21 |
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.