Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer

TL;DR

This paper introduces a sparse optimization-based iterative learning control method that automatically selects relevant basis functions to improve control performance, demonstrated on an industrial printer.

Contribution

It presents a novel sparse optimization approach for basis function selection in ILC, enhancing systematic and automatic control signal design.

Findings

Successfully applied to an industrial flatbed printer

Automatically selects relevant basis functions

Improves tracking performance

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.