Event-Triggered Distributed Model Predictive Control for Platoon Coordination at Hubs in a Transport System

TL;DR

This paper introduces an event-triggered distributed model predictive control approach for coordinating truck platoons at hubs, enabling scalable and efficient scheduling with limited communication, demonstrated through large-scale simulations.

Contribution

It proposes a novel distributed MPC method for hub-based platoon coordination that allows trucks to independently decide waiting times with limited information sharing.

Findings

Effective coordination in large-scale simulations with 100 trucks.

Improved scheduling efficiency at hubs for platooning.

Demonstrated scalability and practicality of the method.

Abstract

This paper considers the problem of hub-based platoon coordination for a large-scale transport system, where trucks have individual utility functions to optimize. An event-triggered distributed model predictive control method is proposed to solve the optimal scheduling of waiting times at hubs for individual trucks. In this distributed framework, trucks are allowed to decide their waiting times independently and only limited information is shared between trucks. Both the predicted reward gained from platooning and the predicted cost for waiting at hubs are included in each truck's utility function. The performance of the coordination method is demonstrated in a simulation with one hundred trucks over the Swedish road network.