Iterative learning control based on extremum seeking

• Sei Zhen Khong ^[1] ; Dragan Nešić ^[2] ; Miroslav Krstic ^[3]
  1. [1] University of Minnesota

     University of Minnesota

     City of Minneapolis, Estados Unidos

     
  2. [2] University of Melbourne

     University of Melbourne

     Australia

     
  3. [3] University of California, USA

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• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 66, 2016, págs. 238-245
• Idioma: inglés
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• Resumen

  • 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.