An Observer Design Method Using Ultra-Local Model for Autonomous Vehicles

Daniel Fenyes, Tamas Hegedus, Vu Van Tan, Peter Gaspar, Peter Gaspar

2023

Abstract

The paper presents a novel observer design algorithm for autonomous vehicles. The technique is based on the combination of a classical linear observer and the ultra-local model. The linear observer is easy to design and it requires only a linear model of the considered system. However, it performs poorly when the linear system cannot cover the system’s dynamics due to nonlinearities or unmodelled dynamics. The ultra-local model aims to compensate for the nonlinear effects and improve the overall performances of the observer. The proposed method is applied to a vehicle-oriented estimation problem: lateral velocity. The operation and the effectiveness of the presented algorithm is demonstrated through several test scenarios in CarSim and also using real-test measurements.

Paper Citation

in Harvard Style

Fenyes D., Hegedus T., Van Tan V. and Gaspar P. (2023). An Observer Design Method Using Ultra-Local Model for Autonomous Vehicles. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 41-49. DOI: 10.5220/0012184300003543

in Bibtex Style

@conference{icinco23,
author={Daniel Fenyes and Tamas Hegedus and Vu Van Tan and Peter Gaspar},
title={An Observer Design Method Using Ultra-Local Model for Autonomous Vehicles},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2023},
pages={41-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012184300003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - An Observer Design Method Using Ultra-Local Model for Autonomous Vehicles
SN - 978-989-758-670-5
AU - Fenyes D.
AU - Hegedus T.
AU - Van Tan V.
AU - Gaspar P.
PY - 2023
SP - 41
EP - 49
DO - 10.5220/0012184300003543
PB - SciTePress