Data-driven dual-loop control for platooning mixed human-driven and automated vehicles

Lan, J. (2023) Data-driven dual-loop control for platooning mixed human-driven and automated vehicles. IET Intelligent Transport Systems, (doi: 10.1049/itr2.12409) (Early Online Publication)

Abstract

This paper considers controlling automated vehicles (AVs) to form a platoon with human-driven vehicles (HVs) under consideration of unknown HV model parameters and propulsion time constants. The proposed design is a data-driven dual-loop control strategy for the ego AVs, where the inner loop controller ensures platoon stability and the outer loop controller keeps a safe inter-vehicular spacing under control input limits. The inner loop controller is a constant-gain state feedback controller solved from a semidefinite program using the online collected data of platooning errors. The outer loop is a model predictive control that embeds a data-driven internal model to predict the future platooning error evolution. The proposed design is evaluated on a mixed platoon with a representative aggressive reference velocity profile, the SFTP-US06 drive cycle. The results confirm efficacy of the design and its advantages over the existing single loop data-driven model predictive control in terms of platoon stability and computational cost.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lan, Dr Jianglin
• Authors: Lan, J.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IET Intelligent Transport Systems
• Publisher: Wiley
• ISSN: 1751-956X
• ISSN (Online): 1751-9578
• Published Online: 26 July 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in IET Intelligent Transport Systems 2023
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
314249		Decarbonising Machine Learning for Safe and Robust Autonomous Systems	Jianglin Lan	Leverhulme Trust (LEVERHUL)	ECF-2021-517	ENG - Autonomous Systems & Connectivity