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 | 1203-1210 |
| Number of pages | 8 |
| 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
Two observers for joint parameter and state estimation are presented in this paper. The observers are based on the Extended Kalman Filter (EKF) or the Square Root Cubature Kalman Filter (SRCuKF) and a Recursive Predictive Error (RPE) method for state and parameter estimation, respectively. Sensitivity models are introduced to compute and minimize a cost functional and then recursively estimate parameter and process covariance values online. The algorithm performance is tested using simulation models of two test benches. Simulation results show that the novel method based on SRCuKF is more accurate than the adaptive EKF and gives improved results with stiff and highly nonlinear systems. A projection algorithm and an adaptive gain for the RPE are introduced to make the complete observer more stable.
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. 1203-1210 7320776.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Online parameter and process covariance estimation using adaptive EKF and SRCuKF approaches
AU - Riva, Mauro Hernan
AU - Beckmann, Daniel
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Two observers for joint parameter and state estimation are presented in this paper. The observers are based on the Extended Kalman Filter (EKF) or the Square Root Cubature Kalman Filter (SRCuKF) and a Recursive Predictive Error (RPE) method for state and parameter estimation, respectively. Sensitivity models are introduced to compute and minimize a cost functional and then recursively estimate parameter and process covariance values online. The algorithm performance is tested using simulation models of two test benches. Simulation results show that the novel method based on SRCuKF is more accurate than the adaptive EKF and gives improved results with stiff and highly nonlinear systems. A projection algorithm and an adaptive gain for the RPE are introduced to make the complete observer more stable.
AB - Two observers for joint parameter and state estimation are presented in this paper. The observers are based on the Extended Kalman Filter (EKF) or the Square Root Cubature Kalman Filter (SRCuKF) and a Recursive Predictive Error (RPE) method for state and parameter estimation, respectively. Sensitivity models are introduced to compute and minimize a cost functional and then recursively estimate parameter and process covariance values online. The algorithm performance is tested using simulation models of two test benches. Simulation results show that the novel method based on SRCuKF is more accurate than the adaptive EKF and gives improved results with stiff and highly nonlinear systems. A projection algorithm and an adaptive gain for the RPE are introduced to make the complete observer more stable.
UR - http://www.scopus.com/inward/record.url?scp=84964330008&partnerID=8YFLogxK
U2 - 10.1109/cca.2015.7320776
DO - 10.1109/cca.2015.7320776
M3 - Conference contribution
AN - SCOPUS:84964330008
SP - 1203
EP - 1210
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 -