Data-Informativity for Data-Driven Supervisory Control of Discrete-Event Systems
Tomofumi Ohtsuka, Kai Cai, Kenji Kashima · 2023
In this paper 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 data set contains sufficient information such that a valid supervisor may be constructed for a family of DES models that all can generate the data set. We then characterize data-informativity with a necessary and sufficient condition, based on which we design an algorithm for its verification.