Multivariable iterative learning control design procedures: from decentralized to centralized, illustrated on an industrial printer

Lennart Blanken (Corresponding author), Tom Oomen

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
20 Downloads (Pure)

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.
Original languageEnglish
Article number8684767
Pages (from-to)1534-1541
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Volume28
Issue number4
DOIs
Publication statusPublished - 1 Jul 2020

Keywords

  • Iterative learning control
  • mechatronics
  • motion control
  • multivariable systems

Fingerprint Dive into the research topics of 'Multivariable iterative learning control design procedures: from decentralized to centralized, illustrated on an industrial printer'. Together they form a unique fingerprint.

Cite this