Rational Basis Functions in Iterative Learning Control for Multivariable Systems

Conference paper (2023)

Authors

Maurice Poot Eindhoven University of Technology
Jim Portegies Eindhoven University of Technology
Dragan Kostić ASMPT Ltd.
Tom Oomen Eindhoven University of Technology, Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Maurice Poot, Jim Portegies, Dragan Kostić, T.A.E. Oomen
DOI: https://doi.org/10.1109/CDC49753.2023.10383932
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.

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