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Cyber-Resilient Data-Driven Event-Triggered Secure Control for Autonomous Vehicles Under False Data Injection Attacks

Yashar Mousavi, Mahsa Tavasoli, Ibrahim Beklan Kucukdemiral, Umit Cali, Abdolhossein Sarrafzadeh, Ali Karimoddini, Afef Fekih

TL;DR

This work tackles secure lateral control for autonomous vehicles under actuator false data injection by marrying data-driven modeling with an event-triggered secure fractional-order sliding mode controller and an extended state observer. Dynamic Mode Decomposition identifies the lateral dynamics from real data, while the event-triggered scheme reduces communication burden; an ESO estimates and compensates actuator attacks in real time. A fractional-order sliding mode controller, augmented with Lyapunov-LMI stability analysis, provides finite-time convergence of sliding surfaces despite delays and attacks, with a three-tier secure controller design (equivalent, switching, composite). Simulations on a realistic AV model show improved lateral tracking, reduced communication load, and effective attack mitigation, demonstrating practical cyber-resilience for safety-critical AV applications.

Abstract

This paper proposes a cyber-resilient secure control framework for autonomous vehicles (AVs) subject to false data injection (FDI) threats as actuator attacks. The framework integrates data-driven modeling, event-triggered communication, and fractional-order sliding mode control (FSMC) to enhance the resilience against adversarial interventions. A dynamic model decomposition (DMD)-based methodology is employed to extract the lateral dynamics from real-world data, eliminating the reliance on conventional mechanistic modeling. To optimize communication efficiency, an event-triggered transmission scheme is designed to reduce the redundant transmissions while ensuring system stability. Furthermore, an extended state observer (ESO) is developed for real-time estimation and mitigation of actuator attack effects. Theoretical stability analysis, conducted using Lyapunov methods and linear matrix inequality (LMI) formulations, guarantees exponential error convergence. Extensive simulations validate the proposed event-triggered secure control framework, demonstrating substantial improvements in attack mitigation, communication efficiency, and lateral tracking performance. The results show that the framework effectively counteracts actuator attacks while optimizing communication-resource utilization, making it highly suitable for safety-critical AV applications.

Cyber-Resilient Data-Driven Event-Triggered Secure Control for Autonomous Vehicles Under False Data Injection Attacks

TL;DR

This work tackles secure lateral control for autonomous vehicles under actuator false data injection by marrying data-driven modeling with an event-triggered secure fractional-order sliding mode controller and an extended state observer. Dynamic Mode Decomposition identifies the lateral dynamics from real data, while the event-triggered scheme reduces communication burden; an ESO estimates and compensates actuator attacks in real time. A fractional-order sliding mode controller, augmented with Lyapunov-LMI stability analysis, provides finite-time convergence of sliding surfaces despite delays and attacks, with a three-tier secure controller design (equivalent, switching, composite). Simulations on a realistic AV model show improved lateral tracking, reduced communication load, and effective attack mitigation, demonstrating practical cyber-resilience for safety-critical AV applications.

Abstract

This paper proposes a cyber-resilient secure control framework for autonomous vehicles (AVs) subject to false data injection (FDI) threats as actuator attacks. The framework integrates data-driven modeling, event-triggered communication, and fractional-order sliding mode control (FSMC) to enhance the resilience against adversarial interventions. A dynamic model decomposition (DMD)-based methodology is employed to extract the lateral dynamics from real-world data, eliminating the reliance on conventional mechanistic modeling. To optimize communication efficiency, an event-triggered transmission scheme is designed to reduce the redundant transmissions while ensuring system stability. Furthermore, an extended state observer (ESO) is developed for real-time estimation and mitigation of actuator attack effects. Theoretical stability analysis, conducted using Lyapunov methods and linear matrix inequality (LMI) formulations, guarantees exponential error convergence. Extensive simulations validate the proposed event-triggered secure control framework, demonstrating substantial improvements in attack mitigation, communication efficiency, and lateral tracking performance. The results show that the framework effectively counteracts actuator attacks while optimizing communication-resource utilization, making it highly suitable for safety-critical AV applications.

Paper Structure

This paper contains 22 sections, 6 theorems, 70 equations, 8 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement and Methodological Framework
  3. Mathematical Modeling of Autonomous Vehicle Lateral Dynamics
  4. State Measurement and Data Acquisition
  5. Efficient Event-Triggered Scheme
  6. Extended State Observer for Actuator Attack Detection and Compensation
  7. Data-Driven Secure Event-Triggered Control Framework
  8. Case A - Transmission Delay Within Triggered Instants
  9. Case B - Transmission Delay Beyond Triggered Instants
  10. Main Results and System Implementation
  11. Data-Driven Modeling via Dynamic Model Decomposition
  12. Secure Control Design and Stability Analysis
  13. Stability Analysis
  14. Secure Controllers Design
  15. Step 1 - Designing the Equivalent Controller $\mathcal{K}$
  16. ...and 7 more sections

Key Result

Theorem 1

Given $e(k)$ in Eq__17 and the event-triggered scheme Eq__16, the error dynamics satisfy: where $\Upsilon$ is a positive-definite weighting matrix, and $\mu \in [0, 1]$ controls the triggering sensitivity. Accordingly, the stability of the event-triggered control system is ensured if the error vector $e(k)$ satisfies the event-triggered condition Eq__16.

Figures (8)

  • Figure 1: Kinematic model of AV lateral dynamic.
  • Figure 2: Cyber-resilient data-driven ETS-FSMC control framework for AVs under FDI attacks.
  • Figure 3: State trajectories under different conditions for Cases I, II, and III.
  • Figure 4: Control input and actuator attack signals for Cases I, II, and III.
  • Figure 5: Sliding surface trajectories for all three cases. The proposed controller maintains stable sliding behavior despite actuator attacks in Case III.
  • ...and 3 more figures

Theorems & Definitions (28)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Remark 5
  • Remark 6
  • Remark 7
  • Remark 8
  • Remark 9
  • Theorem 1
  • ...and 18 more