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Resilient Interval Observer-Based Control for Cooperative Adaptive Cruise Control under FDI Attack

Parisa Ansari Bonab, Elisabeth Andarge Gedefaw, Mohammad Khajenejad

TL;DR

The paper tackles false data injection attacks in cooperative adaptive cruise control (CACC) by integrating a nonlinear Lyapunov-based controller with an interval observer and a neural network-based FDI estimator. The interval observer provides guaranteed upper and lower bounds on the leader's state under bounded measurement noise, while the neural estimator reconstructs the unknown attack in real time to mitigate its impact. An LP-based design yields L1-optimal ISS guarantees for the interval observer, and a semi-global uniformly ultimately bounded stability is established for the closed-loop dynamics. MATLAB/Simulink simulations confirm resilient tracking, accurate attack estimation, and robustness to disturbance and noise, supporting real-world applicability in cyber-physical vehicle networks.

Abstract

Connectivity in connected and autonomous vehicles (CAVs) introduces vulnerability to cyber threats such as false data injection (FDI) attacks, which can compromise system reliability and safety. To ensure resilience, this paper proposes a control framework combining a nonlinear controller with an interval observer for robust state estimation under measurement noise. The observer bounds leader's states, while a neural network-based estimator estimates the unknown FDI attacks in real time. These estimates are then used to mitigate FDI attack effects maintaining safe inter-vehicle spacing. The proposed approach leverages an idea of interval observer-based estimation and merges model-based and learning-based methods to achieve accurate estimations and real-time performance. MATLAB/Simulink results confirm resilient tracking, precise FDI attack estimation, and robustness to noise, demonstrating potential for real-world CACC applications under cyberattacks, disturbance, and bounded measurement noise.

Resilient Interval Observer-Based Control for Cooperative Adaptive Cruise Control under FDI Attack

TL;DR

The paper tackles false data injection attacks in cooperative adaptive cruise control (CACC) by integrating a nonlinear Lyapunov-based controller with an interval observer and a neural network-based FDI estimator. The interval observer provides guaranteed upper and lower bounds on the leader's state under bounded measurement noise, while the neural estimator reconstructs the unknown attack in real time to mitigate its impact. An LP-based design yields L1-optimal ISS guarantees for the interval observer, and a semi-global uniformly ultimately bounded stability is established for the closed-loop dynamics. MATLAB/Simulink simulations confirm resilient tracking, accurate attack estimation, and robustness to disturbance and noise, supporting real-world applicability in cyber-physical vehicle networks.

Abstract

Connectivity in connected and autonomous vehicles (CAVs) introduces vulnerability to cyber threats such as false data injection (FDI) attacks, which can compromise system reliability and safety. To ensure resilience, this paper proposes a control framework combining a nonlinear controller with an interval observer for robust state estimation under measurement noise. The observer bounds leader's states, while a neural network-based estimator estimates the unknown FDI attacks in real time. These estimates are then used to mitigate FDI attack effects maintaining safe inter-vehicle spacing. The proposed approach leverages an idea of interval observer-based estimation and merges model-based and learning-based methods to achieve accurate estimations and real-time performance. MATLAB/Simulink results confirm resilient tracking, precise FDI attack estimation, and robustness to noise, demonstrating potential for real-world CACC applications under cyberattacks, disturbance, and bounded measurement noise.
Paper Structure (14 sections, 4 theorems, 44 equations, 6 figures, 1 table)

This paper contains 14 sections, 4 theorems, 44 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Problem Statement
  4. Mathematical Model of CACC under FDI attacks and Measurement Noise
  5. Proposed Solution
  6. Proposed Control Strategy & FDI Attack Estimation
  7. Observer Design
  8. Stability Analysis
  9. Simulation Results
  10. Proposed Method Without Measurement Noise
  11. Proposed Method With Measurement Noise
  12. Conclusion
  13. Neural Network Details
  14. System Parameters & Computed Gains for the Simulation Example

Key Result

Proposition 1

Khajenejad2025[Correct Interval Framers and Framer Errors] For any generic $n$-dimensional continuous-time dynamic system $\mathcal{G}$, with state $x(t)$ and bounded process and measurement noise signals $w(t)\in \mathcal{W}$ and $v(t) \in \mathcal{V}$, the signals/sequences $\overline{x},\underlin

Figures (6)

  • Figure 1: Leader's position estimation.
  • Figure 2: Distance between lead and following vehicles.
  • Figure 3: FDI attack estimation
  • Figure 4: Leader's position estimation
  • Figure 5: Distance between lead and following vehicles
  • ...and 1 more figures

Theorems & Definitions (6)

  • Definition 1: Interval
  • Proposition 1
  • Proposition 2
  • Proposition 3
  • Theorem 1
  • proof