Paper Info
Title
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 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.
Year
DOI
Venue
2018
10.1109/tcst.2019.2903021
IEEE Transactions on Control Systems and Technology
Field
DocType
Volume
Mathematical optimization,Singular value,Multivariable calculus,Parametric model,Control theory,Gershgorin circle theorem,Robustness (computer science),Iterative learning control,Mathematics
Journal
abs/1806.08550
Citations 
PageRank 
References 
0
0.34
15
Authors
2
Name
Order
Citations
PageRank
Lennart Blanken100.34
Oomen, T.29517.42