Data-Informativity for Data-Driven Supervisory Control of Discrete-Event Systems
成果类型：
Article
署名作者：
Ohtsuka, Tomofumi; Cai, Kai; Kashima, Kenji
署名单位：
Kyoto University; Osaka Metropolitan University
刊物名称：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN/ISSBN：
0018-9286
DOI：
10.1109/TAC.2025.3594610
发表日期：
2026
关键词：
摘要：
In this article, we develop a data-driven approach for supervisory control of discrete-event systems (DES). We consider a setup in which models of DES to be controlled are unknown, but a set of data concerning the behaviors of DES is available. We propose a new concept of data-informativity, which captures the notion that the available dataset contains sufficient information such that a valid supervisor may be constructed for a family of DES models that all can generate the dataset. We then characterize data-informativity with a necessary and sufficient condition, based on which we design an algorithm for its verification. Moreover, if the dataset fails to be informative, then we propose two related new concepts of restricted data-informativity and informatizability. Their characterization conditions and verification algorithms are also presented. Finally, if the dataset is informatizable, then we develop an algorithm to compute the largest subset of control specification for which the dataset is least restricted informative.