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

Abstract

AbstractThis 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.