Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

TLDR

Platooning of CAVs promises transformative benefits such as improved safety, traffic efficiency, and fuel savings, and distributed control schemes that rely only on local information offer scalable coordination without centralized communication. The paper proposes a decomposition framework for modeling, analyzing, and designing CAV platoons from a multi‑agent consensus control perspective. The framework decomposes a platoon into node dynamics, information flow network, distributed controller, and geometry formation, summarizes classic models for each component, discusses performance metrics such as internal stability, stability margin, string stability, and coherence, and reviews typical distributed control techniques including linear consensus, robust, sliding mode, and model predictive control.

Abstract

The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control.