Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer
Systems and Control
2025-05-12 v1 Systems and Control
Abstract
Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.
Cite
@article{arxiv.2505.05835,
title = {Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer},
author = {Tjeerd Ickenroth and Max van Haren and Johan Kon and Max van Meer and Jilles van hulst and Tom Oomen},
journal= {arXiv preprint arXiv:2505.05835},
year = {2025}
}