Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties

Xu Jin, Jian Xin Xu

Research output: Contribution to journalArticlepeer-review

252 Scopus citations

Abstract

In this work, by proposing a Barrier Composite Energy Function (BCEF) method with a novel Barrier Lyapunov Function (BLF), we present a new iterative learning control (ILC) scheme for a class of singleinput single-output (SISO) high order nonlinear systems to deal with output-constrained problems under alignment condition with both parametric and nonparametric system uncertainties. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties satisfying local Lipschitz condition can be effectively handled. Backstepping design with the newly proposed BLF is incorporated in analysis to ensure output constraint not violated. Through rigorous analysis, we show that under this new ILC scheme, uniform convergence of state tracking error is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed ILC scheme.

Original languageEnglish
Pages (from-to)2508-2516
Number of pages9
JournalAutomatica
Volume49
Issue number8
DOIs
StatePublished - Aug 2013

Bibliographical note

Funding Information:
This paper was supported by the SERC Research Grant, No: 092 101 00558 . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Changyun Wen under the direction of Editor Miroslav Krstic.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Alignment condition
  • Barrier composite energy function
  • Iterative learning control
  • Parametric and nonparametric uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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