Data-driven approximation of regions of attraction via an LP-based selection of PWA Lyapunov functions

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Main Authors: Khattabi, Oumayma, Tacchi-Bénard, Matteo, Gulan, Martin, Olaru, Sorin
Format: Preprint
Published: 2026
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author Khattabi, Oumayma
Tacchi-Bénard, Matteo
Gulan, Martin
Olaru, Sorin
author_facet Khattabi, Oumayma
Tacchi-Bénard, Matteo
Gulan, Martin
Olaru, Sorin
contents This paper presents a method to approximate regions of attraction of unknown nonlinear dynamical systems from data. Assuming point-wise evaluations of the vector field and known Lipschitz bounds, a polyhedral uncertainty set of admissible dynamics is constructed. This uncertainty description enables the synthesis of a continuous piece-wise affine Lyapunov candidate via a linear program, enforcing a robust decrease condition for all admissible vector fields. The approach allows certification of a region of attraction consistent with the available data. Numerical examples illustrate the effectiveness of the proposed method in extracting certified regions of attraction from sparse data.
format Preprint
id arxiv_https___arxiv_org_abs_2605_19961
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Data-driven approximation of regions of attraction via an LP-based selection of PWA Lyapunov functions
Khattabi, Oumayma
Tacchi-Bénard, Matteo
Gulan, Martin
Olaru, Sorin
Optimization and Control
Systems and Control
This paper presents a method to approximate regions of attraction of unknown nonlinear dynamical systems from data. Assuming point-wise evaluations of the vector field and known Lipschitz bounds, a polyhedral uncertainty set of admissible dynamics is constructed. This uncertainty description enables the synthesis of a continuous piece-wise affine Lyapunov candidate via a linear program, enforcing a robust decrease condition for all admissible vector fields. The approach allows certification of a region of attraction consistent with the available data. Numerical examples illustrate the effectiveness of the proposed method in extracting certified regions of attraction from sparse data.
title Data-driven approximation of regions of attraction via an LP-based selection of PWA Lyapunov functions
topic Optimization and Control
Systems and Control
url https://arxiv.org/abs/2605.19961