Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking

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141 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. 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
Pages (from-to)16-23
Number of pages8
JournalSystems and Control Letters
Volume89
DOIs
StatePublished - Mar 2016

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.

Keywords

  • Adaptive iterative learning control
  • Consensus
  • Fault tolerant
  • Multi-agent system
  • Nonlinear system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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