Details
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings |
| Place of Publication | Sydney, Australia |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1945-1950 |
| Number of pages | 6 |
| ISBN (electronic) | 9781479977871 |
| Publication status | Published - 4 Nov 2015 |
| Event | IEEE Conference on Control and Applications, CCA 2015 - Sydney, Australia Duration: 21 Sept 2015 → 23 Sept 2015 |
Abstract
Advanced driver assistance systems in modern vehicles have gained interest in the past decades. For most of these systems accurate knowledge about the current driving state, describing the vehicle's stability, and certain parameters is beneficial for improved performance. Especially, a robust estimation of the vehicle's side-slip angle, and, furthermore, knowledge about some influential system parameters, like the vehicle's mass or its moment of inertia, has vast potential to improve the state estimation's accuracy and, therefore, improve the assistance system's performance. In this paper an online estimation of the vehicle's side-slip angle and additional estimation of the mass and moment of inertia, separately and simultaneously is presented using the joint Unscented Kalman Filter. The state estimation results are validated by comparing to measurements taken on a VW Golf VII. The parameter estimation results are verified by comparing to results obtained using a global offline identification algorithm.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings. Sydney, Australia: Institute of Electrical and Electronics Engineers Inc., 2015. p. 1945-1950 7320894.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Joint unscented Kalman filter for state and parameter estimation in vehicle dynamics
AU - Wielitzka, Mark
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Advanced driver assistance systems in modern vehicles have gained interest in the past decades. For most of these systems accurate knowledge about the current driving state, describing the vehicle's stability, and certain parameters is beneficial for improved performance. Especially, a robust estimation of the vehicle's side-slip angle, and, furthermore, knowledge about some influential system parameters, like the vehicle's mass or its moment of inertia, has vast potential to improve the state estimation's accuracy and, therefore, improve the assistance system's performance. In this paper an online estimation of the vehicle's side-slip angle and additional estimation of the mass and moment of inertia, separately and simultaneously is presented using the joint Unscented Kalman Filter. The state estimation results are validated by comparing to measurements taken on a VW Golf VII. The parameter estimation results are verified by comparing to results obtained using a global offline identification algorithm.
AB - Advanced driver assistance systems in modern vehicles have gained interest in the past decades. For most of these systems accurate knowledge about the current driving state, describing the vehicle's stability, and certain parameters is beneficial for improved performance. Especially, a robust estimation of the vehicle's side-slip angle, and, furthermore, knowledge about some influential system parameters, like the vehicle's mass or its moment of inertia, has vast potential to improve the state estimation's accuracy and, therefore, improve the assistance system's performance. In this paper an online estimation of the vehicle's side-slip angle and additional estimation of the mass and moment of inertia, separately and simultaneously is presented using the joint Unscented Kalman Filter. The state estimation results are validated by comparing to measurements taken on a VW Golf VII. The parameter estimation results are verified by comparing to results obtained using a global offline identification algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84964390973&partnerID=8YFLogxK
U2 - 10.1109/cca.2015.7320894
DO - 10.1109/cca.2015.7320894
M3 - Conference contribution
AN - SCOPUS:84964390973
SP - 1945
EP - 1950
BT - 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Sydney, Australia
T2 - IEEE Conference on Control and Applications, CCA 2015
Y2 - 21 September 2015 through 23 September 2015
ER -