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Defense Strategies for Autonomous Multi-agent Systems: Ensuring Safety and Resilience Under Exponentially Unbounded FDI Attacks

Yichao Wang, Mohamadamin Rajabinezhad, Dimitra Panagou, Shan Zuo

TL;DR

This paper tackles false data injection attacks on autonomous multi-agent systems (MAS), focusing on exponentially unbounded FDI (EU-FDI). It proposes the SAAR framework that integrates an attack-resilient observer layer (OL), an attack-resilient compensational signal on the control input layer (CIL), and a quadratic programming (QP) based safety layer using control barrier functions (CBFs). The approach guarantees that the containment error $e_c$ is uniformly ultimately bounded (UUB) and enforces collision avoidance via the CBF constraint $h_{S_{ij}}(x_i,x_j)\\le 0$ for all agent pairs, supported by a Lyapunov-based stability analysis. The authors validate the method with a 3D simulation of a heterogeneous MAS, illustrating resilience against EU-FDI on both OL and CIL and demonstrating practical applicability in safety-critical settings where leader agents define safe regions for followers.

Abstract

False data injection attacks pose a significant threat to autonomous multi-agent systems (MASs). Existing attack-resilient control strategies generally have strict assumptions on the attack signals and overlook safety constraints, such as collision avoidance. In practical applications, leader agents equipped with advanced sensors or weaponry span a safe region to guide heterogeneous follower agents, ensuring coordinated operations while addressing collision avoidance to prevent financial losses and mission failures. This letter addresses these gaps by introducing and solving the safety-aware and attack-resilient (SAAR) control problem under exponentially unbounded false data injection (EU-FDI) attacks. Specifically, a novel attack-resilient observer layer (OL) is first designed to defend against EU-FDI attacks on the OL. Then, an attack-resilient compensational signal is designed to mitigate the adverse effects caused by the EU-FDI attack on control input layer (CIL). Finally, a SAAR controller is designed by solving a quadratic programming (QP) problem integrating control barrier function (CBF) certified collision-free safety constraints. Rigorous Lyapunov-based stability analysis certifies the SAAR controller's effectiveness in ensuring both safety and resilience. This study also pioneers a three-dimensional (3D) simulation of the SAAR containment control problem for heterogeneous MASs, demonstrating its applicability in realistic multi-agent scenarios.

Defense Strategies for Autonomous Multi-agent Systems: Ensuring Safety and Resilience Under Exponentially Unbounded FDI Attacks

TL;DR

This paper tackles false data injection attacks on autonomous multi-agent systems (MAS), focusing on exponentially unbounded FDI (EU-FDI). It proposes the SAAR framework that integrates an attack-resilient observer layer (OL), an attack-resilient compensational signal on the control input layer (CIL), and a quadratic programming (QP) based safety layer using control barrier functions (CBFs). The approach guarantees that the containment error is uniformly ultimately bounded (UUB) and enforces collision avoidance via the CBF constraint for all agent pairs, supported by a Lyapunov-based stability analysis. The authors validate the method with a 3D simulation of a heterogeneous MAS, illustrating resilience against EU-FDI on both OL and CIL and demonstrating practical applicability in safety-critical settings where leader agents define safe regions for followers.

Abstract

False data injection attacks pose a significant threat to autonomous multi-agent systems (MASs). Existing attack-resilient control strategies generally have strict assumptions on the attack signals and overlook safety constraints, such as collision avoidance. In practical applications, leader agents equipped with advanced sensors or weaponry span a safe region to guide heterogeneous follower agents, ensuring coordinated operations while addressing collision avoidance to prevent financial losses and mission failures. This letter addresses these gaps by introducing and solving the safety-aware and attack-resilient (SAAR) control problem under exponentially unbounded false data injection (EU-FDI) attacks. Specifically, a novel attack-resilient observer layer (OL) is first designed to defend against EU-FDI attacks on the OL. Then, an attack-resilient compensational signal is designed to mitigate the adverse effects caused by the EU-FDI attack on control input layer (CIL). Finally, a SAAR controller is designed by solving a quadratic programming (QP) problem integrating control barrier function (CBF) certified collision-free safety constraints. Rigorous Lyapunov-based stability analysis certifies the SAAR controller's effectiveness in ensuring both safety and resilience. This study also pioneers a three-dimensional (3D) simulation of the SAAR containment control problem for heterogeneous MASs, demonstrating its applicability in realistic multi-agent scenarios.
Paper Structure (5 sections, 4 theorems, 38 equations, 6 figures)

This paper contains 5 sections, 4 theorems, 38 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Preliminaries and Problem Formulation
  3. Main Result of SAAR Controller Design
  4. Numerical Simulations
  5. Conclusion

Key Result

Lemma 1

Given Assumption ass: leader follower, the containment control objective is achieved if $\lim_{t \to \infty} e_c(t) = \mathbf{0}$. That is, each follower converges to the convex hull spanned by the leaders.

Figures (6)

  • Figure 1: Pairs.
  • Figure 2: Block diagram of the SAAR control system.
  • Figure 3: Communication topology.
  • Figure 4: Leader-follower 3D motion evolution: (a) $0\,\operatorname{s}$ snapshot. (b) $15.08\,\operatorname{s}$ snapshot (after attack initiation) using the standard control. (c) $15.08\,\operatorname{s}$ snapshot (after attack initiation) using the SAAR control.
  • Figure 5: Containment errors: (a) using conventional control (b) using SAAR control.
  • ...and 1 more figures

Theorems & Definitions (10)

  • Definition 1: rockafellar2015convex
  • Definition 2: khalil2002nonlinear
  • Remark 1
  • Lemma 1: zuo2017output
  • Definition 3
  • Definition 4: ames2014control
  • Definition 5: SAAR Control Problem
  • Lemma 2: zuo2017output
  • Lemma 3
  • Theorem 1