Computation of Neural Networks Lyapunov Functions for Discrete and Continuous Time Systems with Domain of Attraction Maximization

Benjamin Bocquillon, Philippe Feyel, Guillaume Sandou, Pedro Rodriguez-Ayerbe

2020

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.

Paper Citation

in Harvard Style

Bocquillon B., Feyel P., Sandou G. and Rodriguez-Ayerbe 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) - Volume 1: NCTA; ISBN 978-989-758-475-6, SciTePress, pages 471-478. DOI: 10.5220/0010176504710478

in Bibtex Style

@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) - Volume 1: NCTA},
year={2020},
pages={471-478},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010176504710478},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: 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
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