Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings 15th International Symposium on Advanced Vehicle Control - AVEC'22 |
| Seitenumfang | 5 |
| Publikationsstatus | Veröffentlicht - 12 Sept. 2022 |
| Veranstaltung | 15th International Symposium on Advanced Vehicle Control - AVEC'22 - Dauer: 12 Sept. 2022 → 16 Sept. 2022 |
Abstract
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Proceedings 15th International Symposium on Advanced Vehicle Control - AVEC'22. 2022.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Optimized Tuning of an EKF for State and Parameter Estimation in a Semitrailer
AU - Ehlers, Simon Friedrich Gerhard
AU - Sourkounis, Pieris Konrad
AU - Ziaukas, Zygimantas
AU - Kobler, Jan Philipp
AU - Jacob, Hans-Georg
PY - 2022/9/12
Y1 - 2022/9/12
N2 - 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.
AB - 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.
U2 - 10.15488/17784
DO - 10.15488/17784
M3 - Conference contribution
BT - Proceedings 15th International Symposium on Advanced Vehicle Control - AVEC'22
T2 - 15th International Symposium on Advanced Vehicle Control - AVEC'22
Y2 - 12 September 2022 through 16 September 2022
ER -