Data-driven dual-loop control for platooning mixed human-driven and automated vehicles

[摘要] This paper considers controlling automated vehicles (AVs) to form a platoon with human-driven vehicles (HVs) under consideration of unknown HV model parameters and propulsion time constants. The proposed design is a data-driven dual-loop control strategy for the ego AVs, where the inner loop controller ensures platoon stability and the outer loop controller keeps a safe inter-vehicular spacing under control input limits. The inner loop controller is a constant-gain state feedback controller solved from a semidefinite program using the online collected data of platooning errors. The outer loop is a model predictive control that embeds a data-driven internal model to predict the future platooning error evolution. The proposed design is evaluated on a mixed platoon with a representative aggressive reference velocity profile, the SFTP-US06 drive cycle. The results confirm efficacy of the design and its advantages over the existing single loop data-driven model predictive control in terms of platoon stability and computational cost.

[效力级别]  Early Access

[关键词] TRAFFIC-FLOW;CRUISE CONTROL;MODEL;DESIGN