Efficient Construction of Neural Networks Lyapunov Functions with Domain of Attraction Maximization

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

2020

Abstract

This work deals with a new method for computing Lyapunov functions represented by neural networks for autonomous nonlinear systems. Based on the Lyapunov theory and the notion of domain of attraction, we propose an optimization method for determining a Lyapunov function modelled by a neural network while maximizing the domain of attraction. The potential of the proposed method is demonstrated by simulation examples.

Paper Citation

in Harvard Style

Bocquillon B., Feyel P., Sandou G. and Rodriguez-Ayerbe P. (2020). Efficient Construction of Neural Networks Lyapunov Functions with Domain of Attraction Maximization.In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-442-8, pages 174-180. DOI: 10.5220/0009883401740180

in Bibtex Style

@conference{icinco20,
author={Benjamin Bocquillon and Philippe Feyel and Guillaume Sandou and Pedro Rodriguez-Ayerbe},
title={Efficient Construction of Neural Networks Lyapunov Functions with Domain of Attraction Maximization},
booktitle={Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2020},
pages={174-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009883401740180},
isbn={978-989-758-442-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Efficient Construction of Neural Networks Lyapunov Functions with Domain of Attraction Maximization
SN - 978-989-758-442-8
AU - Bocquillon B.
AU - Feyel P.
AU - Sandou G.
AU - Rodriguez-Ayerbe P.
PY - 2020
SP - 174
EP - 180
DO - 10.5220/0009883401740180