Iterative Learning Control Using Stochastic Approximation Theory with Application to a Mechatronic System

In this paper it is shown how Stochastic Approximation theory can be used to derive and analyse well-known Iterative Learning Control algorithms for linear systems. The Stochastic Approximation theory gives conditions that, when satisfied, ensure almost sure convergence of the algorithms to the optimal input in the presence of stochastic disturbances. The practical issues of monotonic convergence and robustness to model uncertainty are considered. Specific choices of the learning matrix are studied, as well as a model-free choice. Moreover, the model-free method is applied to a linear motor system, leading to greatly improved tracking.


Editeur(s):
Lévine, Jean
Müllhaupt, Philippe
Publié dans:
Advances in the Theory of Control, Signals and Systems with Physical Modeling, 49-64
Année
2010
Publisher:
London, Springer
Mots-clefs:
Laboratoires:


Note: Le statut de ce fichier est: Seulement EPFL


 Notice créée le 2010-10-21, modifiée le 2019-04-15

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