Data-Driven Memory-Dependent Abstractions of Dynamical Systems via a Cantor-Kantorovich Metric

• 成果类型: Article
• 署名作者: Banse, Adrien; Romao, Licio; Abate, Alessandro; Jungers, Raphael M.
• 署名单位: Universite Catholique Louvain; Technical University of Denmark; University of Oxford
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3582534
• 发表日期: 2025
• 关键词: Cyber-physical systems; Markov; verification; wasserstein

摘要

The Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this article, 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.