Robustly Learning Regions of Attraction from Fixed Data - Université Grenoble Alpes
Pré-Publication, Document De Travail Année : 2023

Robustly Learning Regions of Attraction from Fixed Data

Apprentissage robuste de régions d'attraction à partir de données fixées

Résumé

While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator representing the system at hand. In this work, a new data-driven Lyapunov analysis framework is proposed. Without using the model or its simulator, the proposed approach can learn a piece-wise affine Lyapunov function with a finite and fixed off-line dataset. The learnt Lyapunov function is robust to any dynamics that are consistent with the off-line dataset, and its computation is based on second order cone programming. Along with the development of the proposed scheme, a slight generalization of classical Lyapunov stability criteria is derived, enabling an iterative inference algorithm to augment the region of attraction.
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Dates et versions

hal-04101941 , version 1 (21-05-2023)
hal-04101941 , version 2 (11-09-2024)

Identifiants

Citer

Matteo Tacchi, Yingzhao Lian, Colin N. Jones. Robustly Learning Regions of Attraction from Fixed Data. 2024. ⟨hal-04101941v2⟩
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