Efficient Robust Control of Mixed Platoon for Improving Fuel Economy and Ride Comfort
Peiyu Zhang, Daxin Tian, Jianshan Zhou, Xuting Duan, Zhengguo Sheng, Dezong Zhao, Dongpu Cao
Abstract
The emergence of connected and automated vehicle technology has improved the operational efficiency of mixed traffic systems. This paper studies a two-tier trajectory optimization problem for mixed platooning to improve fuel efficiency, ride comfort, and operational safety during vehicle operations. The proposed model follows a two-tier control logic to plan the trajectory of platooning vehicles with three objectives, including minimizing fuel consumption, maximizing ride comfort, and enhancing the anti-disturbance performance of the platoon. The first is the planning tier, which aims to design the optimal trajectory for Connected and Automated Vehicles (CAVs) based on the optimal fuel consumption and comfort and obtain the expected acceleration curve of CAV. The second is the control tier, which aims to ensure the safe operation of platooning vehicles in the presence of uncertain disturbances in real time. Specifically, we propose a robust tube MPC control method, which dynamically adjusts the CAV acceleration according to the reference trajectory obtained by the planning tier to resist the effects of uncertain disturbances, and the tracking behaviour of Human-Driven Vehicles (HDVs) is offline solved by the robust optimal velocity model. Finally, we design simulation experiments to verify the effectiveness of the proposed two-tier optimization framework. The experimental results show the effectiveness and advantages of the two-tier framework in terms of fuel economy, ride comfort, and robustness against different noise disturbances.