Control-relevant neural networks for feedforward control with preview: Applied to an industrial flatbed printer

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The performance of feedforward control depends strongly on its ability to compensate for reproducible disturbances. The aim of this paper is to develop a systematic framework for artificial neural networks (ANN) for feedforward control. The method involves three aspects: a new criterion that emphasizes the closed-loop control objective, inclusion of preview to deal with delays and non-minimum phase dynamics, and enabling the use of an iterative learning algorithm to generate training data in view of addressing generalization errors. The approach is illustrated through simulations and experiments on an industrial flatbed printer.

Original languageEnglish
Article number100241
Number of pages11
JournalIFAC Journal of Systems and Control
Volume27
DOIs
Publication statusPublished - 2024

Funding

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

Keywords

  • Feedforward control
  • Iterative learning control
  • Neural networks

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