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Prescribed time reliable platooning control for connected autonomous vehicles using neural network adaptive estimator
Balakrishnan Devanathan1,2, Palanisamy Selvaraj3, Arumugam Suyampulingam4
1Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India.
Scientific Reports
|February 25, 2025
Summary
This study addresses connected autonomous vehicle platooning control under uncertain dynamics and random actuator failures. A robust adaptive observer enhances control system stability and performance, validated by simulations.
Area of Science:
- Control Systems Engineering
- Robotics
- Artificial Intelligence
Background:
- Connected autonomous vehicles (CAVs) present complex control challenges.
- Platooning requires robust control strategies to manage uncertainties.
- Stochastic actuator faults can compromise vehicle safety and system integrity.
Purpose of the Study:
- To develop a prescribed-time platooning control strategy for CAVs.
- To address unknown vehicle dynamics and stochastic actuator faults.
- To enhance the robustness and stability of the control system.
Main Methods:
- Utilized a radial basis function-based adaptive observer for unknown dynamics compensation.
- Incorporated stochastic actuator fault modeling using Bernoulli distribution.
- Applied Lyapunov stability theory and stochastic analysis for system validation.
Main Results:
- The proposed control algorithm demonstrated enhanced robustness against unknown dynamics.
- The system effectively compensated for stochastic actuator faults.
- Simulation results confirmed the stability and effectiveness of the prescribed-time control design.
Conclusions:
- The developed adaptive observer-based control is effective for CAV platooning.
- The methodology provides a robust solution for systems with uncertainties and faults.
- This research contributes to safer and more reliable autonomous vehicle operations.
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