Control-relevant neural networks for feedforward control with preview
Applied to an industrial flatbed printer
Leontine Aarnoudse (Eindhoven University of Technology)
Johan Kon (Eindhoven University of Technology)
Wataru Ohnishi (University of Tokyo)
Maurice Poot (Eindhoven University of Technology)
Paul Tacx (Eindhoven University of Technology)
Nard Strijbosch (Eindhoven University of Technology)
T. Oomen (Eindhoven University of Technology, TU Delft - Team Jan-Willem van Wingerden)
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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.
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