Distributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologies

TL;DR

This paper introduces a distributed model predictive control approach for heterogeneous vehicle platoons with unidirectional communication, ensuring stability and effective coordination without prior knowledge of the set point.

Contribution

It proposes a novel DMPC algorithm with a stability-guaranteeing terminal constraint for heterogeneous vehicle platoons in unidirectional topologies.

Findings

Successfully stabilizes heterogeneous vehicle platoons

Demonstrates effectiveness through passenger car simulations

Provides explicit conditions for stability based on cost weights

Abstract

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 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 proposed DMPC.