Iterative learning control based on extremum seeking

Sei Zhen Khong, Dragan Nešić, Miroslav Krstić

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

This paper proposes a non-model based approach to iterative learning control (ILC) via extremum seeking. Single-input-single-output discrete-time nonlinear systems are considered, where the objective is to recursively construct an input such that the corresponding system output tracks a prescribed reference trajectory as closely as possible on finite horizon. The problem is formulated in terms of extremum seeking control, which is amenable to a range of local and global optimisation methods. Contrary to the existing ILC literature, the formulation allows the initial condition of each iteration to be incorporated as an optimisation variable to improve tracking. Sufficient conditions for convergence to the reference trajectory are provided. The main feature of this approach is that it does not rely on knowledge about the system's model to perform iterative learning control, in contrast to most results in the literature.

Original languageEnglish (US)
Pages (from-to)238-245
Number of pages8
JournalAutomatica
Volume66
DOIs
StatePublished - Apr 1 2016

Keywords

  • Extremum seeking
  • Iterative learning control
  • Local and global optimisation
  • Non-model based approach

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