Decentralized Tube Model Predictive Control for Arbitrarily Mixed Vehicle Platoons with HDV Uncertainties

In the control problem of a mixed vehicle platoon including connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) in an arbitrary sequence, the behaviors of HDVs can not be described and predicted by accurate models because of disturbances and modeling errors. Considering the uncertainties of HDVs, a decentralized tube model predictive control (MPC) algorithm based on the feedback control and feedforward control is proposed for CAVs in the mixed vehicle platoon. Firstly, we construct a generic nominal system model and an actual system model for each sub-platoon based on the linearized kinematics of HDVs and CAVs. Secondly, the tube invariant set sequence centered on the nominal system trajectories is constructed with the feedforward control, and then the actual trajectories of all vehicles in each sub-platoon are restricted in the tube with the feedback control. Finally, numerical simulations are given to verify that the proposed tube MPC algorithm can overcome the uncertainties of HDVs to achieve formation control of the mixed vehicle platoon.