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Pré-Publication, Document De Travail Année : 2023

Robust data-driven Lyapunov analysis with fixed data

Analyse de Lyapunov robuste à partir de données fixées


In this era of digitalization, data has widely been used in control engineering. While stability analysis is a mainstay for control science, most stability analysis tools still require explicit knowledge of the model or a high-fidelity simulator. 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. Along the development of proposed scheme, the Lyapunov stability criterion is generalized. This generalization enables an iterative algorithm to augment the region of attraction.
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Dates et versions

hal-04101941 , version 1 (21-05-2023)



Yingzhao Lian, Matteo Tacchi, Colin N. Jones. Robust data-driven Lyapunov analysis with fixed data. 2023. ⟨hal-04101941⟩
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