Combining Model-based and Data-driven Observer Designs for Sideslip Angle Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0198479" target="_blank" >RIV/00216305:26210/26:0198479 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11107409" target="_blank" >https://ieeexplore.ieee.org/document/11107409</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3595282" target="_blank" >10.1109/ACCESS.2025.3595282</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining Model-based and Data-driven Observer Designs for Sideslip Angle Estimation

Popis výsledku v původním jazyce

The vehicle side slip angle represents a key indicator of dynamic stability. Elevated values of the side slip angle may indicate a loss of stability or undesired vehicle behaviors such as understeering or oversteering. With the increased use of advanced driver assistance systems (ADAS), the need for accurate estimation of the side slip angle has become increasingly critical. This quantity in general needs to be indirectly measured or estimated, with the latter often representing a more cost-effective and more reliable approach. This is usually done by simple observer design, e.g., Kalman filter, which requires a well-parameterized system dynamics model. In this work we explore Machine Learning techniques in combination with a budget hardware inertial measurement unit to estimate the sideslip angle. This is done independently of the actual vehicle configuration, i.e., vehicle load and tires used. We model the system dynamics with a traditional Luenberger Observer, Long-short-term memory, Gated recurrent unit neural networks, and their combination, and investigate possible performance benefits when incorporating well-known physical relations. The results demonstrate that a well-designed combination of model-based and data-driven approaches can achieve high estimation accuracy even without the need for large datasets, which are typically required when employing purely data-driven methods. The performance of the proposed sideslip angle estimator under different driving conditions and tire configurations is validated with real-world measurement data.

Název v anglickém jazyce

Combining Model-based and Data-driven Observer Designs for Sideslip Angle Estimation

Popis výsledku anglicky

The vehicle side slip angle represents a key indicator of dynamic stability. Elevated values of the side slip angle may indicate a loss of stability or undesired vehicle behaviors such as understeering or oversteering. With the increased use of advanced driver assistance systems (ADAS), the need for accurate estimation of the side slip angle has become increasingly critical. This quantity in general needs to be indirectly measured or estimated, with the latter often representing a more cost-effective and more reliable approach. This is usually done by simple observer design, e.g., Kalman filter, which requires a well-parameterized system dynamics model. In this work we explore Machine Learning techniques in combination with a budget hardware inertial measurement unit to estimate the sideslip angle. This is done independently of the actual vehicle configuration, i.e., vehicle load and tires used. We model the system dynamics with a traditional Luenberger Observer, Long-short-term memory, Gated recurrent unit neural networks, and their combination, and investigate possible performance benefits when incorporating well-known physical relations. The results demonstrate that a well-designed combination of model-based and data-driven approaches can achieve high estimation accuracy even without the need for large datasets, which are typically required when employing purely data-driven methods. The performance of the proposed sideslip angle estimator under different driving conditions and tire configurations is validated with real-world measurement data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

4.8.

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

151838-151849

Kód UT WoS článku

001566975200015

EID výsledku v databázi Scopus

2-s2.0-105013323644