TR2017-050

A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics


    •  Benosman, M., "A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics", American Control Conference (ACC), April 2017.
      BibTeX TR2017-050 PDF
      • @inproceedings{Benosman2017apr,
      • author = {Benosman, Mouhacine},
      • title = {A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics},
      • booktitle = {American Control Conference (ACC)},
      • year = 2017,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2017-050}
      • }
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  • Research Area:

    Dynamical Systems

This paper presents a novel approach to achieve online multivariable hybrid optimization of response maps associated to set-valued dynamical systems, without requiring the use of averaging theory. In particular, we propose a prescriptive framework for the analysis and design of a class of adaptive control architectures based on neural networks (NN) and learning dynamics described by hybrid dynamical systems (HDS). The NNs are used as model-free gradient approximators that are online tuned in order to obtain an arbitrarily precise estimation on a compact set of the gradient of the response map of the system under control. For the closed-loop system a semi-global practical asymptotic stability result is obtained, and the results are illustrated via numerical examples.