Automatic Basis Function Selection in Iterative Learning Control
A Sparsity-Promoting Approach Applied to an Industrial Printer
Tjeerd Ickenroth (Eindhoven University of Technology)
Max Van Haren (Eindhoven University of Technology)
Johan Kon (Eindhoven University of Technology)
Max Van Meer (Eindhoven University of Technology)
Jilles Van Hulst (Eindhoven University of Technology)
T. Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
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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.
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