Distributed Model Predictive Control for Heterogeneous Vehicle Platoons Under Unidirectional Topologies

This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between the predicted and assumed trajectories. Together with this penalization, an equality-based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of the proposed DMPC.

I. Introduction

The platooning of autonomous vehicles has received considerable attention in recent years [1]–[7]. Most of this attention is due to its potential to significantly benefit road transportation, including improving traffic efficiency, enhancing road safety, and reducing fuel consumption [1], [2]. The main objective of the platoon control is to ensure all the vehicles in a group move at the same speed while maintaining a prespecified distance between any consecutive followers [5]–[7].