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

Jian Xin Xu, Xu Jin

Research output: Contribution to journalArticlepeer-review

125 Scopus citations


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
Issue number5
StatePublished - May 2013


  • 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


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