Adversary Detection and Resilient Control for Multi-Agent Systems

TL;DR

This paper introduces an adversary detection method and a resilient control framework for multi-agent systems, ensuring safety and liveness despite adversarial agents under spatiotemporal constraints.

Contribution

It develops a proactive adversary detection mechanism and a resilient QP-based controller for multi-agent systems, enhancing safety and robustness against adversaries.

Findings

Detection mechanism accurately identifies adversarial agents.

Resilient controller maintains safety and liveness constraints.

Simulation validates effectiveness of the proposed methods.

Abstract

This paper presents an adversary detection mechanism and a resilient control framework for multi-agent systems under spatiotemporal constraints. Safety in multi-agent systems is typically addressed under the assumption that all agents collaborate to ensure the forward invariance of a desired safe set. This work analyzes agent behaviors based on certain behavior metrics, and designs a proactive adversary detection mechanism based on the notion of the critical region for the system operation. In particular, the presented detection mechanism not only identifies adversarial agents, but also ensures all-time safety for intact agents. Then, based on the analysis and detection results, a resilient QP-based controller is presented to ensure safety and liveness constraints for intact agents. Simulation results validate the efficacy of the presented theoretical contributions.