Iterative learning control based on extremum seeking

Sei Zhen Khong, Dragan Nešić, Miroslav Krstić

Research output: Contribution to journalArticlepeer-review

35 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

Bibliographical note

Funding Information:
This work was supported by the Swedish Research Council through the LCCC Linnaeus Centre and the Australian Research Council . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Warren E. Dixon under the direction of Editor Andrew R. Teel.

Funding Information:
Dragan Nešić is a Professor in the Department of Electrical and Electronic Engineering (DEEE) at The University of Melbourne, Australia. He received his BE degree in Mechanical Engineering from The University of Belgrade, Yugoslavia in 1990, and his Ph.D. degree from Systems Engineering, RSISE, Australian National University, Canberra, Australia in 1997. Since February 1999 he has been with The University of Melbourne. His research interests include networked control systems, discrete-time, sampled-data and continuous-time nonlinear control systems, input-to-state stability, extremum seeking control, applications of symbolic computation in control theory, hybrid control systems, and so on. He was awarded a Humboldt Research Fellowship (2003) by the Alexander von Humboldt Foundation, an Australian Professorial Fellowship (2004–2009) and Future Fellowship (2010–2014) by the Australian Research Council. He is a Fellow of IEEE and a Fellow of IEAust. He is currently a Distinguished Lecturer of CSS, IEEE (2008–). He served as an Associate Editor for the journals Automatica, IEEE Transactions on Automatic Control, Systems and Control Letters and European Journal of Control.

Publisher Copyright:
© 2016 Elsevier Ltd. All rights reserved.

Keywords

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

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