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

被引：0

作者：

Bocquillon, Benjamin ^{[1
]}

Feyel, Philippe ^{[1
]}

Sandou, Guillaume ^{[2
]}

Rodriguez-Ayerbe, Pedro ^{[2
]}

机构：

[1] Safran Elect & Def, 100 Ave Paris, Massy, France

[2] Univ Paris Saclay, L2S, CNRS, Cent Supelec, 3 Rue Joliot Curie, F-91192 Gif Sur Yvette, France

来源：

PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE (IJCCI) | 2020年

关键词：

Lyapunov Function; Domain of Attraction; Optimization; Neural Network; Nonlinear System; CONSTRUCTION; STABILITY;

D O I：

10.5220/0010176504710478

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：471 / 478

页数：8