Details
| Original language | English |
|---|---|
| Title of host publication | 2017 American Control Conference, ACC 2017 |
| Place of Publication | Seattle, USA |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5780-5786 |
| Number of pages | 7 |
| ISBN (electronic) | 9781509059928 |
| Publication status | Published - 29 Jun 2017 |
| Event | 2017 American Control Conference, ACC 2017 - Seattle, United States Duration: 24 May 2017 → 26 May 2017 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
Abstract
State estimators are used to reconstruct current plant states based on information received from plant sensors and the use of a mathematical model. The typically applied Kalman filter derivatives require knowledge about the noise statistics affecting system states and measurements. These are often unknown and inaccurate parameterization may lead to decreased filter performance or even filter divergence. In this paper, a comparison between two covariance estimation methods is presented. The offline time-varying autocovariance Least-Square (LS) method is compared to the online adaptive Square-Root Unscented Kalman Filter (SRUKF). Both methods are evaluated in simulations and experiments using a pendubot w.r.t. robustness against random covariance initializations and state estimation accuracy. The results show that with both methods the filter performance can remarkably be improved.
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
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2017 American Control Conference, ACC 2017. Seattle, USA: Institute of Electrical and Electronics Engineers Inc., 2017. p. 5780-5786 7963856 (Proceedings of the American Control Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Comparison of covariance estimation using autocovariance LS method and adaptive SRUKF
AU - Riva, Mauro Hernan
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2017/6/29
Y1 - 2017/6/29
N2 - State estimators are used to reconstruct current plant states based on information received from plant sensors and the use of a mathematical model. The typically applied Kalman filter derivatives require knowledge about the noise statistics affecting system states and measurements. These are often unknown and inaccurate parameterization may lead to decreased filter performance or even filter divergence. In this paper, a comparison between two covariance estimation methods is presented. The offline time-varying autocovariance Least-Square (LS) method is compared to the online adaptive Square-Root Unscented Kalman Filter (SRUKF). Both methods are evaluated in simulations and experiments using a pendubot w.r.t. robustness against random covariance initializations and state estimation accuracy. The results show that with both methods the filter performance can remarkably be improved.
AB - State estimators are used to reconstruct current plant states based on information received from plant sensors and the use of a mathematical model. The typically applied Kalman filter derivatives require knowledge about the noise statistics affecting system states and measurements. These are often unknown and inaccurate parameterization may lead to decreased filter performance or even filter divergence. In this paper, a comparison between two covariance estimation methods is presented. The offline time-varying autocovariance Least-Square (LS) method is compared to the online adaptive Square-Root Unscented Kalman Filter (SRUKF). Both methods are evaluated in simulations and experiments using a pendubot w.r.t. robustness against random covariance initializations and state estimation accuracy. The results show that with both methods the filter performance can remarkably be improved.
UR - http://www.scopus.com/inward/record.url?scp=85027005585&partnerID=8YFLogxK
U2 - 10.23919/acc.2017.7963856
DO - 10.23919/acc.2017.7963856
M3 - Conference contribution
AN - SCOPUS:85027005585
T3 - Proceedings of the American Control Conference
SP - 5780
EP - 5786
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Seattle, USA
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -