Data-Driven Modeling and Robust Control of Mixed Vehicle Platoons Based on Tube Model Predictive Control

In order to improve road traffic safety and efficiency, this paper studies the modeling and control problem of the mixed vehicle platoon consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). Firstly, a data-driven linear model of the mixed vehicle platoon is constructed by the Extended Dynamic Mode Decomposition (EDMD) algorithm to approximate the infinite-dimensional Koopman operator. Different from the existing related work, we consider model mismatch errors in the data-driven linear model. Secondly, a tube model predictive controller is designed for the mixed vehicle platoon so that the actual state trajectories of all vehicles in the platoon are constrained in a tube centering on the nominal trajectory, which converges to the desired state. Finally, it is verified by numerical simulation that the proposed data-driven modeling method can accurately model the system, and the designed tube MPC controller can overcome the negative effects of model mismatch errors to achieve the platoon control objective.