Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1547-1553 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9781509028733 |
| ISBN (Print) | 978-1-5090-2874-0 |
| Publikationsstatus | Veröffentlicht - 28 Juni 2017 |
| Veranstaltung | 56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australien Dauer: 12 Dez. 2017 → 15 Dez. 2017 |
Abstract
State estimation is applicable to almost all areas of engineering and science. Applications that include a physical-parametric model of a system are candidates for state estimation. These estimators reconstruct the system states based on a system model and information received from the system sensors. The most widely applied state estimators are the Kalman Filter (KF) derivatives. These filters use a parametric system model, system measurements and input information, and require knowledge about the noise statistics affecting the system. These noise statistics are often unknown and inaccurate filter tuning may lead to decreased filter performance or even filter divergence. These estimators can be extended to estimate parameters. However, insufficient system excitation can cause parameter estimation drifts. In this paper, a sensitivity-based adaptive Square-Root Un-scented Kalman Filter (SRUKF) is presented. This filter estimates system states, parameters and noise covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts during phases of insufficient system excitation. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint SRUKF.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Entscheidungswissenschaften (sonstige)
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Mathematik (insg.)
- Steuerung und Optimierung
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2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. S. 1547-1553.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Sensitivity-based adaptive SRUKF for online state, parameter, and process covariance estimation
AU - Riva, Mauro Hernan
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
PY - 2017/6/28
Y1 - 2017/6/28
N2 - State estimation is applicable to almost all areas of engineering and science. Applications that include a physical-parametric model of a system are candidates for state estimation. These estimators reconstruct the system states based on a system model and information received from the system sensors. The most widely applied state estimators are the Kalman Filter (KF) derivatives. These filters use a parametric system model, system measurements and input information, and require knowledge about the noise statistics affecting the system. These noise statistics are often unknown and inaccurate filter tuning may lead to decreased filter performance or even filter divergence. These estimators can be extended to estimate parameters. However, insufficient system excitation can cause parameter estimation drifts. In this paper, a sensitivity-based adaptive Square-Root Un-scented Kalman Filter (SRUKF) is presented. This filter estimates system states, parameters and noise covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts during phases of insufficient system excitation. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint SRUKF.
AB - State estimation is applicable to almost all areas of engineering and science. Applications that include a physical-parametric model of a system are candidates for state estimation. These estimators reconstruct the system states based on a system model and information received from the system sensors. The most widely applied state estimators are the Kalman Filter (KF) derivatives. These filters use a parametric system model, system measurements and input information, and require knowledge about the noise statistics affecting the system. These noise statistics are often unknown and inaccurate filter tuning may lead to decreased filter performance or even filter divergence. These estimators can be extended to estimate parameters. However, insufficient system excitation can cause parameter estimation drifts. In this paper, a sensitivity-based adaptive Square-Root Un-scented Kalman Filter (SRUKF) is presented. This filter estimates system states, parameters and noise covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts during phases of insufficient system excitation. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint SRUKF.
UR - http://www.scopus.com/inward/record.url?scp=85046122314&partnerID=8YFLogxK
U2 - 10.1109/cdc.2017.8263871
DO - 10.1109/cdc.2017.8263871
M3 - Conference contribution
AN - SCOPUS:85046122314
SN - 978-1-5090-2874-0
SP - 1547
EP - 1553
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -