  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.
