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Memory-based event-triggered fuzzy path tracking security control for autonomous vehicles against stochastic false

Longxin Guan¹, Lie Guo², Linli Xu³

¹School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

ISA Transactions

|May 8, 2026

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This study introduces a robust security control method for autonomous vehicles (AVs) to counter network vulnerabilities. The novel approach enhances path tracking performance by reducing lateral and yaw errors, improving AV safety and efficiency.

Area of Science:

Control Systems Engineering
Cybersecurity for Autonomous Systems
Automotive Engineering

Background:

Autonomous vehicles (AVs) rely on Controller Area Network (CAN) systems, which are susceptible to bandwidth limitations and False Data Injection (FDI) attacks.
These vulnerabilities pose significant risks to the path tracking performance and overall safety of AVs.
Existing control methods may not adequately address the combined challenges of network constraints and security threats.

Purpose of the Study:

To develop a novel event-triggered robust security control method for CAN-dependent AVs.
To enhance the safety, efficiency, and path tracking performance of AVs against network vulnerabilities and FDI attacks.
To propose a method that effectively balances network load and control performance.

Main Methods:

Keywords:

Autonomous vehicles False data injection attacks Interval type-2 Takagi-Sugeno fuzzy model Memory-based event-triggered mechanism Path tracking security control

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An interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model was developed to represent lateral vehicle dynamics.
An IT2 T-S fuzzy observer was designed for estimating vehicle lateral speed.
A memory-based event-triggered mechanism (METM) was introduced to optimize network usage and control effectiveness.
An observer-based fuzzy security controller was designed using Lyapunov-Krasovskii functionals (LKFs) and linear matrix inequalities (LMIs) for H∞ stability.

Main Results:

The proposed event-triggered robust security control method demonstrated significant improvements in path tracking.
Simulation and hardware-in-loop (HiL) results showed reductions in root mean square lateral error by over 31% and yaw angle error by over 11% compared to a standard H∞ controller.
The METM effectively managed network burden while maintaining high control performance.

Conclusions:

The novel event-triggered robust security control method effectively enhances the safety and efficiency of autonomous vehicle control systems.
The proposed approach provides a robust solution for mitigating the impact of network bandwidth limitations and FDI attacks on AV path tracking.
This research offers a valuable contribution to secure and reliable autonomous driving systems.