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.


Editor(s):
Lévine, Jean
Müllhaupt, Philippe
Published in:
Advances in the Theory of Control, Signals and Systems with Physical Modeling, 49-64
Year:
2010
Publisher:
London, Springer
Keywords:
Laboratories:


Note: The status of this file is: EPFL only


 Record created 2010-10-21, last modified 2018-09-13

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