State-constrained iterative learning control for a class of MIMO systems

Jian Xin Xu, Xu Jin

Research output: Contribution to journalArticlepeer-review

160 Scopus citations

Abstract

In this note, we present a novel iterative learning control (ILC) method for a class of state-constrained multi-input multi-output (MIMO) nonlinear system under state alignment condition with both parametric and nonparametric uncertainties. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties satisfying local Lipschitz condition can be effectively handled. Barrier Composite Energy Function (BCEF) scheme with a novel Barrier Lyapunov Function is proposed to facilitate the analysis of state tracking error convergence while satisfying the state constraints. In the end, an illustrative example is shown to demonstrate the efficacy of the proposed ILC method.

Original languageEnglish
Pages (from-to)1322-1327
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume58
Issue number5
DOIs
StatePublished - May 2013

Keywords

  • Alignment condition
  • Barrier composite energy function (CEF)
  • Iterative learning control (ILC)
  • Parametric and nonparametric uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'State-constrained iterative learning control for a class of MIMO systems'. Together they form a unique fingerprint.

Cite this