Multivariable Iterative Learning Control Design Procedures: from Decentralized to Centralized, Illustrated on an Industrial Printer

TL;DR

This paper develops multivariable Iterative Learning Control methods that balance modeling effort and performance, addressing interaction through nominal models or robustness, demonstrated on an industrial printer.

Contribution

It introduces new ILC design procedures for multivariable systems that handle interaction via nominal models or robustness, with practical trade-offs.

Findings

Methods using structured singular value and Gershgorin bounds improve robustness.

Trade-offs between modeling effort and control performance are demonstrated.

Case study on an industrial printer validates the approaches.

Abstract

Iterative Learning Control (ILC) enables high control performance through learning from measured data, using only limited model knowledge in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this paper is to develop a range of approaches for multivariable ILC, where specific attention is given to addressing interaction. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits the modeling budget and control requirements. This trade-off is demonstrated in a case study on an industrial flatbed printer.