Platooning control of connected and automated vehicles (CAVs) has emerged as a promising strategy to enhance fuel economy, traffic efficiency and safety. One of the main challenges in coordinating multiple vehicles is executing safe and efficient cooperative control, while considering the inherent nonlinearities in vehicle dynamics. This study aims to design a data-driven optimal distributed leader-tracking control for heterogeneous vehicle platoon in the traffic scenario. Every vehicle within the platoon is treated as a multi-input-multi-output system, exhibiting nonlinear dynamics. The distributed control system is developed using reinforcement learning (RL) algorithm that learns the optimal control policies from the vehicle system data without requiring prior knowledge of vehicle dynamics. This method ensures that all platoon vehicles reliably follow the leader's behaviour, even in the presence of unknown and nonlinear dynamics. Simulation results involving a platoon of heterogeneous and nonlinear CAVs demonstrate the effectiveness of the proposed cooperative controller.
Data-Driven Optimal Cooperative Control for Vehicle Platoons with Unknown Dynamics
24.09.2024
1307735 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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