Monotonic convergence of iterative learning control for uncertain systems using a time-varying Q-filter

Douglas A. Bristow, Andrew G. Alleyne

Research output: Contribution to journalConference article

Abstract

Time-varying Q-filtering in Iterative Learning Control (ILC) has demonstrated potential performance benefits over time-invariant Q-filtering. In this paper, LTV Q-filtering of ILC is considered for uncertain systems. Sufficient conditions for stability and the important monotonic convergence property are developed for the uncertain system. A class of LTV Q-filters that has particular benefit for rapid motion trajectories is presented, and monotonic convergence conditions are developed. The developed conditions highlight a relationship that the bandwidth can be increased locally and decreased elsewhere to localize high performance at specific times. These conditions are also iteration-length invariant and allow for significant design freedom after analysis enabling online modification of the LTV Q-filter.

Original languageEnglish (US)
Article numberWeA06.1
Pages (from-to)171-177
Number of pages7
JournalProceedings of the American Control Conference
Volume1
StatePublished - Sep 1 2005
Event2005 American Control Conference, ACC - Portland, OR, United States
Duration: Jun 8 2005Jun 10 2005

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Uncertain systems
Trajectories
Bandwidth

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Monotonic convergence of iterative learning control for uncertain systems using a time-varying Q-filter. / Bristow, Douglas A.; Alleyne, Andrew G.

In: Proceedings of the American Control Conference, Vol. 1, WeA06.1, 01.09.2005, p. 171-177.

Research output: Contribution to journalConference article

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