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Erschienen in: Automotive Innovation 4/2023

10.11.2023

Path Tracking Control for Autonomous Truck with Dual Modular Chassis

verfasst von: Tao Liu, Yanhua Shen, Kaidi Wang

Erschienen in: Automotive Innovation | Ausgabe 4/2023

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Abstract

This study focuses on enhancing the agility and path tracking capabilities of autonomous trucks equipped with dual 4WIS-4WID modular chassis. To address the challenges associated with these versatile vehicles, a comprehensive approach is presented. Firstly, a communication framework is devised, utilizing a hierarchical combination of two fieldbus systems. This framework facilitates adaptive marshalling, allowing effective communication and coordination among the various modular components of the autonomous truck. Secondly, a reference path generation strategy is proposed. This strategy relates the motion paths of the truck's body to its modular chassis. Reference paths for the modular chassis are derived based on the center of mass, effectively resolving the issue of differing motion paths. To tackle the path tracking problem for dual modular chassis, a cooperative path tracking controller is developed. This controller is designed using the kinematic model of the autonomous truck, enabling adaptive control through online adjustments of controller parameters based on measured input–output data. Simulation and real vehicle testing validate the proposed path tracking controller. In the dual modular chassis path tracking simulation, the maximum lateral position error and the maximum yaw angle error of truck body at different speeds are 0.082 m and 0.007 rad, respectively. In the real vehicle test, the maximum lateral position error is 0.194 m, and the maximum yaw angle error is 0.071 rad. These results demonstrate the practicality and effectiveness of the controller in real-world applications.
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Metadaten
Titel
Path Tracking Control for Autonomous Truck with Dual Modular Chassis
verfasst von
Tao Liu
Yanhua Shen
Kaidi Wang
Publikationsdatum
10.11.2023
Verlag
Springer Nature Singapore
Erschienen in
Automotive Innovation / Ausgabe 4/2023
Print ISSN: 2096-4250
Elektronische ISSN: 2522-8765
DOI
https://doi.org/10.1007/s42154-023-00268-7

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