Data-driven multivariable ILC: enhanced performance by eliminating L and Q filters

J.J. Bolder, S.J. Kleinendorst, T.A.E. Oomen

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)
6 Downloads (Pure)


Iterative learning control (ILC) algorithms enable high-performance control design using only approximate models of the system. To deal with severe modeling errors, a robustness filter Q is typically employed. Irrespective of the large performance enhancement, these approaches thus require a modeling effort and are subject to a performance/robustness tradeoff. The aim of this paper is to develop a fully data-driven ILC approach that does not require a modeling effort and mitigates the performance/robustness tradeoff. The main idea is to replace the use of a model by dedicated experiments on the system. Convergence conditions are developed in a finite-time framework, and insight in the convergence aspects is presented using a frequency-domain analysis. Extensions to increase the convergence speed are proposed. The developed framework is validated through experiments on a multivariable industrial flatbed printer. Both increased performance and robustness are demonstrated in a comparison with closely related model-based ILC algorithms.

Original languageEnglish
Pages (from-to)3728-3751
Number of pages24
JournalInternational Journal of Robust and Nonlinear Control
Issue number12
Publication statusPublished - 1 Aug 2018


  • Data-driven control
  • Iterative learning control
  • Optimal ILC
  • data-driven control
  • iterative learning control
  • optimal ILC


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