Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Xiao-Dong Li
  • Mang-Mang Lv
  • John K. L. Ho


Original languageEnglish
Pages (from-to)2279-2289
Journal / PublicationInternational Journal of Systems Science
Issue number10
Online published6 Jan 2015
Publication statusPublished - 2016


In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.

Research Area(s)

  • adaptive iterative learning control, non-parametric uncertainties, non-repetitive trajectory tracking, nonlinear continuous systems

Citation Format(s)