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This paper considers controlling automated vehicles (AVs) to form a platoonwith human-driven vehicles (HVs) under consideration of unknown HV modelparameters and propulsion time constants. The proposed design is a data-drivendual-loop control strategy for the ego AVs, where the inner loop controllerensures platoon stability and the outer loop controller keeps a safeinter-vehicular spacing under control input limits. The inner loop controlleris a constant-gain state feedback controller solved from a semidefinite program(SDP) using the online collected data of platooning errors. The outer loop is amodel predictive control (MPC) that embeds a data-driven internal model topredict the future platooning error evolution. The proposed design is evaluatedon a mixed platoon with a representative aggressive reference velocity profile,the SFTP-US06 Drive Cycle. The results confirm efficacy of the design and itsadvantages over the existing single loop data-driven MPC in terms of platoonstability and computational cost.

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