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

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.