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Authors: Benjamin Bocquillon 1 ; Philippe Feyel 1 ; Guillaume Sandou 2 and Pedro Rodriguez-Ayerbe 2

Affiliations: 1 Safran Electronics & Defense, 100 avenue de Paris, Massy, France ; 2 Université Paris-Saclay, CentraleSupélec, CNRS, L2S, 3 rue Joliot Curie, 91192 Gif-Sur-Yvette, France

Keyword(s): Lyapunov Function, Domain of Attraction, Optimization, Neural Network, Nonlinear System.

Abstract: This contribution deals with a new approach for computing Lyapunov functions represented by neural networks for nonlinear discrete-time systems to prove asymptotic stability. Based on the Lyapunov theory and the notion of domain of attraction, the proposed approach deals with an optimization method for determining a Lyapunov function modeled by a neural network while maximizing the domain of attraction. Several simulation examples are presented to illustrate the potential of the proposed method.

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Paper citation in several formats:
Bocquillon, B., Feyel, P., Sandou, G., Rodriguez-Ayerbe and P. (2020). Computation of Neural Networks Lyapunov Functions for Discrete and Continuous Time Systems with Domain of Attraction Maximization. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA; ISBN 978-989-758-475-6; ISSN 2184-3236, SciTePress, pages 471-478. DOI: 10.5220/0010176504710478

@conference{ncta20,
author={Benjamin Bocquillon and Philippe Feyel and Guillaume Sandou and Pedro Rodriguez{-}Ayerbe},
title={Computation of Neural Networks Lyapunov Functions for Discrete and Continuous Time Systems with Domain of Attraction Maximization},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA},
year={2020},
pages={471-478},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010176504710478},
isbn={978-989-758-475-6},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA
TI - Computation of Neural Networks Lyapunov Functions for Discrete and Continuous Time Systems with Domain of Attraction Maximization
SN - 978-989-758-475-6
IS - 2184-3236
AU - Bocquillon, B.
AU - Feyel, P.
AU - Sandou, G.
AU - Rodriguez-Ayerbe, P.
PY - 2020
SP - 471
EP - 478
DO - 10.5220/0010176504710478
PB - SciTePress

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