Data-Driven Event-Triggered Sliding Mode Secure Control for Autonomous Vehicles Under Actuator Attacks
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a data-driven control for secure autonomous vehicle steering, addressing actuator attacks and communication limits. The method ensures vehicle stability and safety despite cyber threats.
Area Of Science
- Control Systems Engineering
- Robotics
- Cybersecurity
Background
- Autonomous vehicles face challenges in secure lateral control due to modeling complexities, limited communication, and actuator attacks.
- Ensuring vehicle stability and safety under adversarial conditions is critical for widespread adoption.
Purpose Of The Study
- To develop a comprehensive data-driven, event-triggered secure lateral control strategy for autonomous vehicles.
- To address stabilization issues arising from modeling uncertainties and actuator attacks.
Main Methods
- Dynamic Model Decomposition (DMD) from data to characterize vehicle lateral dynamics.
- Event-triggered transmission scheme to manage communication load for limited bandwidth networks.
- Sliding mode control design to ensure security against actuator attacks.
Main Results
- A novel secure control scheme integrating data-driven modeling and model-based control.
- Demonstrated ability to actively counteract malicious effects from actuator attacks.
- Validation of effectiveness through comparative case studies.
Conclusions
- The proposed event-triggered secure lateral control effectively enhances autonomous vehicle safety and stability.
- This approach combines data-driven insights with robust control for resilient autonomous systems.
- The method offers a viable solution for secure autonomous vehicle operation in complex environments.
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