Convergence analysis of iterative learning control with uncertain initial conditions

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

14 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages960-963
Volume2
Publication statusPublished - 2002

Publication series

Name
Volume2

Conference

TitleProceedings of the 4th World Congress on Intelligent Control and Automation
PlaceChina
CityShanghai
Period10 - 14 June 2002

Abstract

This paper explores convergence problem of iterative learning control (ILC) for linear discrete-time multivariable systems with uncertain initial conditions from two-dimensional (2-D) notion. The iterative learning process is described by a 2-D learning model, which includes both the system dynamics and the learning process. A simple ILC rule is used and the effect of tracking errors against varying initial conditions is investigated. Based on 2-D system theory, the conditions of the convergence of the learning control rules are proposed. It it shown that the learning rule can be guaranteed to converge with respect to small perturbations of the system parameters even though the initial condition of each iteration is variable.

Citation Format(s)

Convergence analysis of iterative learning control with uncertain initial conditions. / Fang, Yong; Soh, Yeng Chai; Feng, Gary G.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). Vol. 2 2002. p. 960-963.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review