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 (SDP) using the online collected data of platooning errors. The outer loop is a model predictive control (MPC) 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 MPC in terms of platoon stability and computational cost.

翻译：本文研究在未知人类驾驶车辆模型参数及推进时间常数的条件下，控制自动驾驶车辆与人类驾驶车辆组成队列的问题。针对自车AV，提出一种数据驱动双环控制策略：内环控制器确保队列稳定性，外环控制器在控制输入受限条件下保持安全车间距。内环控制器采用基于在线采集的队列误差数据通过半定规划求解的恒增益状态反馈控制器；外环控制器为嵌入数据驱动内模的模型预测控制，用于预测未来队列误差演变。通过包含代表性强加速参考速度曲线（SFTP-US06驾驶循环）的混合队列对所提设计进行评估。结果验证了该设计的有效性，并表明其在队列稳定性和计算成本方面优于现有单环数据驱动MPC方法。