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Fuzzy-Model-Based Lateral Control for Networked Autonomous Vehicle Systems Under Hybrid Cyber-Attacks.

Zhi Lian, Peng Shi, Cheng-Chew Lim

IEEE Transactions on Cybernetics

|March 14, 2022

Summary

This summary is machine-generated.

This study presents a new control method for autonomous vehicles to ensure stable path following despite network issues and cyber-attacks. The asynchronous resilient event-triggered scheme enhances system stability and performance.

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Area of Science:

Control Systems Engineering
Robotics
Networked Systems

Background:

Autonomous vehicles require robust lateral control systems.
Network-induced issues like cyber-attacks and time delays challenge system stability.
Existing control methods struggle with nonlinear dynamics and hybrid attacks.

Purpose of the Study:

To develop a novel control strategy for networked autonomous vehicles.
To address lateral control under disturbances and network vulnerabilities.
To ensure smooth path following for nonlinear autonomous vehicles.

Main Methods:

A fuzzy-model-based system represents nonlinear networked vehicles under hybrid cyber-attacks.
An asynchronous resilient event-triggered scheme (ETS) is proposed to mitigate network burden and cyber-attack effects.
A dynamic output-feedback control method is developed.

Main Results:

Conditions for global exponential stability of the closed-loop switched fuzzy system are derived.
The proposed output-feedback controller and resilient asynchronous ETS ensure system stability.
Demonstrated effectiveness and merits of the new control design techniques through examples.

Conclusions:

The proposed method effectively handles lateral control for networked autonomous vehicles.
The asynchronous resilient ETS enhances system resilience against cyber-attacks and network delays.
This research contributes to the safety and reliability of autonomous driving systems.