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Observer-Based Data-Driven Consensus Control for Nonlinear Multi-Agent Systems against DoS and FDI attacks

Yi Zhang, Bin Lei, Mohamadamin Rajabinezhad, Caiwen Ding, Shan Zuo

TL;DR

This work addresses consensus in nonlinear multi-agent systems under simultaneous false data injection and denial-of-service attacks. It develops an observer-based data-driven MFAC framework that employs attack-resilient observers to estimate FDI signals, external disturbances, and lumped disturbances, plus a DoS compensation mechanism. A discrete-time compact-form dynamic linearization model and contraction-mapping stability analysis guarantee that the neighborhood consensus error remains bounded by terms determined by disturbance and estimation bounds. Numerical simulations for leaderless and leader-follower scenarios demonstrate substantially improved attack resilience over conventional MFAC methods.

Abstract

Existing data-driven control methods generally do not address False Data Injection (FDI) and Denial-of-Service (DoS) attacks simultaneously. This letter introduces a distributed data-driven attack-resilient consensus problem under both FDI and DoS attacks and proposes a data-driven consensus control framework, consisting of a group of comprehensive attack-resilient observers. The proposed group of observers is designed to estimate FDI attacks, external disturbances, and lumped disturbances, combined with a DoS attack compensation mechanism. A rigorous stability analysis of the approach is provided to ensure the boundedness of the distributed neighborhood estimation consensus error. The effectiveness of the approach is validated through numerical examples involving both leaderless consensus and leader-follower consensus, demonstrating significantly improved resilient performance compared to existing data-driven control approaches.

Observer-Based Data-Driven Consensus Control for Nonlinear Multi-Agent Systems against DoS and FDI attacks

TL;DR

This work addresses consensus in nonlinear multi-agent systems under simultaneous false data injection and denial-of-service attacks. It develops an observer-based data-driven MFAC framework that employs attack-resilient observers to estimate FDI signals, external disturbances, and lumped disturbances, plus a DoS compensation mechanism. A discrete-time compact-form dynamic linearization model and contraction-mapping stability analysis guarantee that the neighborhood consensus error remains bounded by terms determined by disturbance and estimation bounds. Numerical simulations for leaderless and leader-follower scenarios demonstrate substantially improved attack resilience over conventional MFAC methods.

Abstract

Existing data-driven control methods generally do not address False Data Injection (FDI) and Denial-of-Service (DoS) attacks simultaneously. This letter introduces a distributed data-driven attack-resilient consensus problem under both FDI and DoS attacks and proposes a data-driven consensus control framework, consisting of a group of comprehensive attack-resilient observers. The proposed group of observers is designed to estimate FDI attacks, external disturbances, and lumped disturbances, combined with a DoS attack compensation mechanism. A rigorous stability analysis of the approach is provided to ensure the boundedness of the distributed neighborhood estimation consensus error. The effectiveness of the approach is validated through numerical examples involving both leaderless consensus and leader-follower consensus, demonstrating significantly improved resilient performance compared to existing data-driven control approaches.
Paper Structure (7 sections, 6 theorems, 20 equations, 6 figures)

This paper contains 7 sections, 6 theorems, 20 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Control System Design and Stability Analysis
  4. Simulation Results
  5. Leaderless Consensus Control
  6. Leader-follower Tracking Control
  7. CONCLUSION

Key Result

Lemma 1

Let the canonical basis of the vectorial space $\mathbb{R}^p$ for all $p \geq 1$ be defined by $E_p = \{ e_p(i) \mid e_p(i) = (0, \dots, 0, \underset{i}{1}, 0, \dots, 0)^{\mathrm{T}}, i = 1, \dots, p \};$ and $f(x): \mathbb{R}^p \to \mathbb{R}^p$ be a function continuous on $[a,b] \in \mathbb{R}^p$

Figures (6)

  • Figure 1: Communication graph. (a) Leaderless consensus control scenario; (b) Leader-follower tracking control scenario.
  • Figure 2: FDI and DoS attacks.
  • Figure 3: Performance of $y_{i}$ (top); and Estimation errors $\tilde{\boldsymbol{\chi}}_i$ using the proposed method under attacks (bottom).
  • Figure 4: FDI and DoS attacks.
  • Figure 5: Performance of $y_{i}$ (top); and Estimation errors $\tilde{\boldsymbol{\chi}}_i$ using the proposed method under attacks (bottom).
  • ...and 1 more figures

Theorems & Definitions (11)

  • Remark 1
  • Lemma 1: phanomchoeng2010bounded Mean Value Theorem for a Vector Function
  • Lemma 2
  • proof
  • Remark 2
  • Theorem 1
  • proof
  • Lemma 3: bell1965gershgorin Gersgorin Disk Theorem
  • Lemma 4: ortega2000iterative
  • Theorem 2
  • ...and 1 more