Data-Driven Safety Certificates of Infinite Networks with Unknown Models and Interconnection Topologies

TL;DR

This paper presents a novel data-driven, compositional method for certifying the safety of infinite networks with unknown models and topologies, using storage certificates to handle complexity.

Contribution

It introduces innovative compositional data-driven conditions to construct barrier certificates for infinite networks without requiring detailed knowledge of interconnection topology.

Findings

Successfully applied to two physical infinite networks with unknown models.

Eliminates the need for traditional dissipativity condition checks.

Provides correctness guarantees for network safety.

Abstract

Infinite networks are complex interconnected systems comprising a countably infinite number of subsystems, for which no fixed upper bound on the number of participating subsystems is specified a priori since it may vary over time as agents join or leave (e.g., vehicles in traffic). In such scenarios, the presence of infinitely many subsystems within the network renders the existing analysis frameworks tailored for finite networks inapplicable to infinite ones. This paper is concerned with offering a data-driven approach, within a compositional framework, for the safety certification of infinite networks with both unknown mathematical models and unknown interconnection topologies. Given the immense computational complexity stemming from the extensive dimension of infinite networks, our approach capitalizes on the joint dissipativity-type properties of subsystems, characterized by storage certificates. We introduce innovative compositional data-driven conditions to construct a barrier certificate for the infinite network leveraging storage certificates of its unknown subsystems derived from data, while offering correctness guarantees for network safety. We demonstrate that our compositional data-driven reasoning eliminates the requirement for checking the traditional dissipativity condition, which typically mandates precise knowledge of the interconnection topology. We illustrate our data-driven results on two physical infinite networks with unknown models and interconnection topologies.