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

TL;DR

This paper introduces a data-driven dual-loop control method for automated vehicles in mixed platoons with human-driven vehicles, enhancing stability and safety under uncertain conditions.

Contribution

It proposes a novel dual-loop control strategy combining SDP-based inner loop and MPC-based outer loop, specifically designed for mixed vehicle platooning with unknown parameters.

Findings

Improves platoon stability with unknown human-driven vehicle models.

Reduces computational cost compared to single-loop MPC.

Effective under aggressive driving profiles like the SFTP-US06 Drive Cycle.

Abstract

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.