Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults

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

6 Scopus citations

Abstract

In this work, we present a new distributed adaptive iterative learning control (AILC) scheme for a class of high-order nonlinear multi-agent systems (MAS) under alignment condition with both parametric and nonparametric system uncertainties, where the actuators may be faulty and the control input gain functions are not fully known. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties can be effectively handled. Backstepping design with the composite energy function (CEF) structure is used in the discussion. Through rigorous analysis, we show that under this new AILC scheme, uniform convergence of agents output tracking error over the iteration domain is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed AILC scheme.

Original languageEnglish
Title of host publication2016 American Control Conference, ACC 2016
Pages1253-1258
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Conference

Conference2016 American Control Conference, ACC 2016
Country/TerritoryUnited States
CityBoston
Period7/6/167/8/16

Bibliographical note

Publisher Copyright:
© 2016 American Automatic Control Council (AACC).

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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