Reliable State Estimation in a Truck-Semitrailer Combination using an Artificial Neural Network-Aided Extended Kalman Filter

verfasst von

Jan-Hendrik Ewering, Zygimantas Ziaukas, Simon Friedrich Gerhard Ehlers, Thomas Seel

Abstract

Advanced driver assistance systems are critically dependent on reliable and accurate information regarding a vehicles' driving state. For estimation of unknown quantities, model-based and learning-based methods exist, but both suffer from individual limitations. On the one hand, model-based estimation performance is often limited by the models' accuracy. On the other hand, learning-based estimators usually do not perform well in 'unknown' conditions (bad generalization), which is particularly critical for semitrailers as their payload changes significantly in operation. To the best of the authors' knowledge, this work is the first to analyze the capability of state-of-the-art estimators for semitrailers to generalize across 'unknown' loading states. Moreover, a novel hybrid Extended Kalman Filter (H-EKF) that takes advantage of accurate Artificial Neural Network (ANN) estimates while preserving reliable generalization capability is presented. It estimates the articulation angle between truck and semitrailer, lateral tire forces and the truck steering angle utilizing sensor data of a standard semitrailer only. An experimental comparison based on a full-scale truck-semitrailer combination indicates the superiority of the H-EKF compared to a state-of-the-art Extended Kalman Filter and an ANN estimator.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Typ

Aufsatz in Konferenzband

Seiten

456-463

Anzahl der Seiten

8

Publikationsdatum

2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerung und Optimierung, Modellierung und Simulation

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2406.14028 (Zugang: Offen)
https://doi.org/10.23919/ECC64448.2024.10590814 (Zugang: Geschlossen)