Data-based control of Logical Networks

TL;DR

This paper explores data-driven methods for controlling Boolean networks, focusing on reachability, equilibria, safe control, and output regulation, using limited data without requiring explicit model identification.

Contribution

It introduces novel techniques for analyzing and controlling Boolean control networks solely from data, bypassing the need for explicit model knowledge.

Findings

Effective evaluation of reachability and equilibria from limited data

Successful safe control and output regulation without model identification

Demonstrated applicability on Boolean control network examples

Abstract

In recent years, data-driven approaches have become increasingly pervasive across all areas of control engineering. However, the applications of data-based techniques to Boolean control networks (BCNs) are still very limited. In this paper we aim to fill this gap, by exploring the possibility of evaluating some basic features, i.e., reachability and equilibria, and of solving two fundamental control problems, i.e., safe control and output regulation, for a BCN, leveraging only a limited amount of data generated by the network, without knowing or identifying its model.