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Learning Sparse Rational Feedforward Controllers

Conference Paper (2025)
Author(s)

Tjeerd Ickenroth (Eindhoven University of Technology)

T.A.E. Oomen (Eindhoven University of Technology, TU Delft - Mechanical Engineering)

Research Group
Team Jan-Willem van Wingerden
DOI related publication
https://doi.org/10.1109/CDC57313.2025.11312107 Final published version
More Info
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Publication Year
2025
Language
English
Research Group
Team Jan-Willem van Wingerden
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Pages (from-to)
7873-7878
Publisher
IEEE
ISBN (electronic)
979-8-3315-2627-6
Event
64th Conference on Decision and Control (CDC 2025)<br/> (2025-12-09 - 2025-12-12), Rio de Janeiro, Brazil
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21
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Abstract

Iterative Learning Control (ILC) with basis function techniques are capable of improving tracking performance and task flexibility. The aim of this paper is to design a systematic approach to enable automatic rational basis function selection for feedforward learning. A sparse optimization framework is proposed to identify the most relevant rational basis functions from a large candidate set. The ILC algorithm that employs sparse optimization is able to automatically select relevant rational basis functions and is validated on an example motion system.

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