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arXiv · 2405.08353

Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric

Adrien Banse·Licio Romao·Alessandro Abate·Raphaël M. Jungers
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Abstract

Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven abstraction mechanism based on a novel metric between Markov models. Our approach is based purely on observing output labels of the underlying dynamics, thus opening the road for a fully data-driven approach to construct abstractions. Another feature of the proposed approach is the use of memory to better represent the dynamics in a given region of the state space. We show through numerical examples the usefulness of the proposed methodology.

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Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers. 2024-05-14. Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric. https://arxiv.org/abs/2405.08353

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