Robust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control

TL;DR

This paper introduces a robust tube model predictive control framework for mixed traffic platoons that effectively manages prediction uncertainties of human-driven vehicles, reducing computational and communication burdens compared to traditional MPC methods.

Contribution

A novel robust tube MPC approach is proposed for mixed traffic platoon control, improving efficiency and reliability by dynamically bounding trajectory deviations without frequent replanning.

Findings

The proposed method effectively bounds vehicle trajectories under uncertainty.

Simulation results show reduced computational and communication loads.

The framework maintains platoon stability despite prediction errors.

Abstract

The design of cooperative adaptive cruise control is critical in mixed traffic flow, where connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist. Compared with pure CAVs, the major challenge is how to handle the prediction uncertainty of HDVs, which can cause significant state deviation of CAVs from planned trajectories. In most existing studies, model predictive control (MPC) is utilized to replan CAVs' trajectories to mitigate the deviation at each time step. However, as the replan process is usually conducted by solving an optimization problem with information through inter-vehicular communication, MPC methods suffer from heavy computational and communicational burdens. To address this limitation, a robust platoon control framework is proposed based on tube MPC in this paper. The prediction uncertainty is dynamically mitigated by the feedback control and restricted inside a set with a high probability. When the uncertainty exceeds the set or additional external disturbance emerges, the feedforward control is triggered to plan a ``tube'' (a sequence of the set), which can bound CAVs' actual trajectories. As the replan process is usually not required, the proposed method is much more efficient regarding computation and communication, compared with the MPC method. Comprehensive simulations are provided to validate the effectiveness of the proposed framework.