Iterative learning control for systems with nonparametric uncertainties under alignment condition

Xu Jin, Deqing Huang, Jian Xin Xu

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations

Abstract

In this work, by incorporating the so-called alignment condition, a novel ILC scheme is proposed for a class of nonlinear systems with nonparametric local Lipschitz continuous (LLC) uncertainties to perform full state tracking tasks. A new Lyapunov-like energy function is adopted to facilitate the ILC design as well as property analysis, and thus achieve the asymptotical convergence of tracking error. More advantages of the proposed design approach lie in that it can handle the scenarios of state-dependent LLC input gain and high-order systems easily. In the end, an illustrative example is simulated to demonstrate the efficacy of the proposed ILC scheme.

Original languageEnglish
Article number6426998
Pages (from-to)3942-3947
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
DOIs
StatePublished - 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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