Abstract
In this study, we present a systematic procedure to compute the identified model parameter uncertainties as functions of the statistics of input and output experimental data obtained using the celebrated Eigensystem Realization Algorithm (ERA). An Unscented Transformation (UT) is applied to map the error statistics from the input-output test signal space of the test data to the plant parameter space. It is shown that a computationally efficient algorithm is obtained by an application of the unscented transformation in a high dimensional space. Outputs of the algorithm include the mean and covariance estimates of the identified plant parameters obtained through the Observer/Kalman Filter Identification (OKID) calculations followed by ERA. Numerical simulations and comparisons with Monte-Carlo error statistics demonstrate the efficacy of the unscented transformation presented in this paper.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 461-473 |
| Number of pages | 13 |
| Journal | Advances in the Astronautical Sciences |
| Volume | 147 |
| State | Published - Jan 1 2013 |
| Event | 2012 AAS Jer-Nan Juang Astrodynamics Symposium - College Station, TX, United States Duration: Jun 24 2012 → Jun 26 2012 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Space and Planetary Science
Cite this
}
Uncertainty quantification of the eigensystem realization algorithm using the unscented transform. / Diz, Martin; Majji, Manoranjan; Singla, Puneet.
In: Advances in the Astronautical Sciences, Vol. 147, 01.01.2013, p. 461-473.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Uncertainty quantification of the eigensystem realization algorithm using the unscented transform
AU - Diz, Martin
AU - Majji, Manoranjan
AU - Singla, Puneet
PY - 2013/1/1
Y1 - 2013/1/1
N2 - In this study, we present a systematic procedure to compute the identified model parameter uncertainties as functions of the statistics of input and output experimental data obtained using the celebrated Eigensystem Realization Algorithm (ERA). An Unscented Transformation (UT) is applied to map the error statistics from the input-output test signal space of the test data to the plant parameter space. It is shown that a computationally efficient algorithm is obtained by an application of the unscented transformation in a high dimensional space. Outputs of the algorithm include the mean and covariance estimates of the identified plant parameters obtained through the Observer/Kalman Filter Identification (OKID) calculations followed by ERA. Numerical simulations and comparisons with Monte-Carlo error statistics demonstrate the efficacy of the unscented transformation presented in this paper.
AB - In this study, we present a systematic procedure to compute the identified model parameter uncertainties as functions of the statistics of input and output experimental data obtained using the celebrated Eigensystem Realization Algorithm (ERA). An Unscented Transformation (UT) is applied to map the error statistics from the input-output test signal space of the test data to the plant parameter space. It is shown that a computationally efficient algorithm is obtained by an application of the unscented transformation in a high dimensional space. Outputs of the algorithm include the mean and covariance estimates of the identified plant parameters obtained through the Observer/Kalman Filter Identification (OKID) calculations followed by ERA. Numerical simulations and comparisons with Monte-Carlo error statistics demonstrate the efficacy of the unscented transformation presented in this paper.
UR - http://www.scopus.com/inward/record.url?scp=84899093021&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899093021&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84899093021
VL - 147
SP - 461
EP - 473
JO - Advances in the Astronautical Sciences
JF - Advances in the Astronautical Sciences
SN - 1081-6003
ER -