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

Abstract

Iterative learning control (ILC) enables high control performance through learning from the measured data, using only limited model knowledge in the form of a nominal parametric model to guarantee convergence. The aim of this brief 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, which provide a different tradeoff between modeling requirements, i.e., modeling effort and cost and achievable performance. This allows an appropriate choice in view of modeling budget and performance requirements. The tradeoff is demonstrated in a case study on an industrial printer.