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Robust Convergence of High-Order Adaptive Iterative Learning Control Against Iteration-Varying Uncertainties

  • Zirong Guo
  • , Deyuan Meng*
  • , Jingyao Zhang
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, adaptive iterative learning control (AILC) algorithms using a high-order law for linear time-varying (LTV) systems with iteration-varying uncertainties are proposed. Sufficient conditions are derived to guarantee the robust convergence of tracking error such that the bounded-input-bounded-output stability of LTV systems can be achieved. Extensions of established AILC results to nonlinear systems are further developed. Numerical simulations are implemented to validate the effectiveness of the theoretical results.

Original languageEnglish
Title of host publicationProceedings of 2019 Chinese Intelligent Systems Conference - Volume I
EditorsYingmin Jia, Junping Du, Weicun Zhang
PublisherSpringer Verlag
Pages591-598
Number of pages8
ISBN (Print)9789813296817
DOIs
StatePublished - 2020
EventChinese Intelligent Systems Conference, CISC 2019 - Haikou, China
Duration: 26 Oct 201927 Oct 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume592
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceChinese Intelligent Systems Conference, CISC 2019
Country/TerritoryChina
CityHaikou
Period26/10/1927/10/19

Keywords

  • Adaptive iterative learning control
  • Iteration-varying uncertainties
  • Linear time-varying system

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