Marking Data-Informativity and Data-Driven Supervisory Control of Discrete-Event Systems

TL;DR

This paper introduces a data-driven method for designing nonblocking supervisors in discrete-event systems without known models, using data to determine when a valid supervisor can be constructed.

Contribution

It formalizes the concept of marking data-informativity and develops algorithms for verification and control synthesis based on data in unknown DES.

Findings

Defined marking data-informativity and related concepts.

Developed algorithms for verification and control synthesis.

Identified conditions under which data suffices for supervisor design.

Abstract

In this paper we develop a data-driven approach for marking nonblocking 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 ask the question: Under what conditions of the available data set can a valid marking noblocking supervisor be designed for the unknown DES to satisfy a given specification? Answering this question, we identify and formalize a novel concept called marking data-informativity. Moreover, we design an algorithm for the verification of this concept. Next, if the data set fails to be marking informative, we propose two related new concepts of restricted marking data-informativity and marking informatizability. Finally, we develop an algorithm to compute the largest subset of control specification for which the data set is least restricted marking informative.