Resilience to Non-Compliance in Coupled Cooperating Systems

TL;DR

This paper develops a control framework for coupled agents that maintains safety despite non-compliance, by analyzing resilience bounds and demonstrating effectiveness through epidemic network simulations.

Contribution

It introduces a method to quantify and ensure resilience to non-compliance in coupled systems using collaborative safety conditions and CBF-based control.

Findings

Resilience bounds depend on neighborhood state and network dynamics.

Agents can tolerate a certain level of non-compliance while maintaining safety.

Simulation confirms the theoretical resilience limits in epidemic models.

Abstract

This letter explores the implementation of a safe control law for systems of dynamically coupled cooperating agents. Under a CBF-based collaborative safety framework, we examine how the maximum safety capability for a given agent, which is computed using a collaborative safety condition, influences safety requests made to neighbors. We provide conditions under which neighbors may be resilient to non-compliance of neighbors to safety requests, and compute an upper bound for the total amount of non-compliance an agent is resilient to, given its 1-hop neighborhood state and knowledge of the network dynamics. We then illustrate our results via simulation on a networked susceptible-infected-susceptible (SIS) epidemic model.