Optimized Tuning of an EKF for State and Parameter Estimation in a Semitrailer

verfasst von

Simon Friedrich Gerhard Ehlers, Pieris Konrad Sourkounis, Zygimantas Ziaukas, Jan Philipp Kobler, Hans-Georg Jacob

Abstract

The Extended Kalman Filter (EKF) is a well-known method for state and parameter estimation in vehicle dynamics. However, for tuning the EKF, knowledge about the process and measurement noise is needed, which is usually unknown. Tuning the noise parameters manually is very time consuming, especially for systems with many states. Automated optimization based on the filtering errors promises less application time and better estimation performance, but also requires computing resources. This work presents two approaches for estimating the noise parameters of an EKF: A particle swarm optimization (PSO) and a gradient-based optimization. The EKF is applied to a nonlinear vehicle model of a tractor-semitrailer for estimating the steering and articulation angle as well as lateral and vertical tire forces based on real measurement data with different trailer loadings. Both methods are compared to each other to achieve the best estimation performance.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Fachgebiet Leistungselektronik und Antriebsregelung

Externe Organisation(en)

BPW Bergische Achsen KG

Typ

Aufsatz in Konferenzband

Anzahl der Seiten

5

Publikationsdatum

12.09.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

Elektronische Version(en)

https://doi.org/10.15488/17784 (Zugang: Offen)
https://tech.jsae.or.jp/paperinfo/en/content/conf2022-01.022/ (Zugang: Geschlossen)