Details
| Original language | English |
|---|---|
| Title of host publication | 2016 American Control Conference, ACC 2016 |
| Place of Publication | Boston, USA |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4513-4519 |
| Number of pages | 7 |
| ISBN (electronic) | 9781467386821 |
| Publication status | Published - 28 Jul 2016 |
| Event | 2016 American Control Conference, ACC 2016 - Boston, United States Duration: 6 Jul 2016 → 8 Jul 2016 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| Volume | 2016-July |
| ISSN (Print) | 0743-1619 |
Abstract
A novel observer for state, parameter and process covariance estimation is presented in this paper. The new observer estimates system states using a Square-Root Unscented Kalman Filter (SRUKF) and by employing the Recursive Prediction-Error (RPE) method, unknown parameters and covariances are identified online. Two experimental applications based on an underactuated planar robot are included to demonstrate the algorithm performance. Additionally, sensitivity models for the SRUKF are derived. Results show that the online process covariance estimation improves the observer convergence and reduces parameter estimation bias.
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2016 American Control Conference, ACC 2016. Boston, USA: Institute of Electrical and Electronics Engineers Inc., 2016. p. 4513-4519 7526063 (Proceedings of the American Control Conference; Vol. 2016-July).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Adaptive Unscented Kalman Filter for Online State, Parameter, and Process Covariance Estimation
AU - Riva, Mauro Hernan
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2016/7/28
Y1 - 2016/7/28
N2 - A novel observer for state, parameter and process covariance estimation is presented in this paper. The new observer estimates system states using a Square-Root Unscented Kalman Filter (SRUKF) and by employing the Recursive Prediction-Error (RPE) method, unknown parameters and covariances are identified online. Two experimental applications based on an underactuated planar robot are included to demonstrate the algorithm performance. Additionally, sensitivity models for the SRUKF are derived. Results show that the online process covariance estimation improves the observer convergence and reduces parameter estimation bias.
AB - A novel observer for state, parameter and process covariance estimation is presented in this paper. The new observer estimates system states using a Square-Root Unscented Kalman Filter (SRUKF) and by employing the Recursive Prediction-Error (RPE) method, unknown parameters and covariances are identified online. Two experimental applications based on an underactuated planar robot are included to demonstrate the algorithm performance. Additionally, sensitivity models for the SRUKF are derived. Results show that the online process covariance estimation improves the observer convergence and reduces parameter estimation bias.
UR - http://www.scopus.com/inward/record.url?scp=84992159476&partnerID=8YFLogxK
U2 - 10.1109/acc.2016.7526063
DO - 10.1109/acc.2016.7526063
M3 - Conference contribution
AN - SCOPUS:84992159476
T3 - Proceedings of the American Control Conference
SP - 4513
EP - 4519
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Boston, USA
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -