Monotonic convergence of iterative learning control systems with variable pass length

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Original languageEnglish
Pages (from-to)409-422
Number of pages14
JournalInternational journal of control
Volume90
Issue number3
Publication statusPublished - 4 Mar 2017

Abstract

A growing number of researchers consider iterative learning control (ILC) a promising tool for numerous control problems in biomedical application systems. We will briefly discuss why classical ILC theory is technically too restrictive for some of these applications. Subsequently, we will extend the classical ILC design in the lifted systems framework to the class of repetitive trajectory tracking tasks with variable pass length. We will analyse the closed-loop dynamics for two standard learning laws, and we will discuss in which sense the tracking error can be reduced by which controller design strategies. Necessary and sufficient conditions for monotonic convergence will be derived. We then summarise all results in a set of practical controller design guidelines. Finally, a simulation study is presented, which demonstrates the usefulness of these guidelines and illustrates the special dynamics that occur in variable pass length learning.

Keywords

    Iterative learning control, biomedical applications, lifted system framework, monotonic convergence, non-uniform trial duration

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Monotonic convergence of iterative learning control systems with variable pass length. / Seel, Thomas; Schauer, Thomas; Raisch, Jörg.
In: International journal of control, Vol. 90, No. 3, 04.03.2017, p. 409-422.

Research output: Contribution to journalArticleResearchpeer review

Seel T, Schauer T, Raisch J. Monotonic convergence of iterative learning control systems with variable pass length. International journal of control. 2017 Mar 4;90(3):409-422. doi: 10.1080/00207179.2016.1183172
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