Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC

Yu Zhao; Zhijie Sun; S. Joe Qin; Tianyou Chai

doi:10.1109/acc.2012.6315683

Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC

Yu Zhao
, Zhijie Sun
, S. Joe Qin
, Tianyou Chai

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

In this paper, a non-stationary Kalman filter parametrization of subspace identification models is adopted to deal with finite data windows. We show that the non-stationary Kalman filter parametrization is the solution to the least squares estimation of the Markov parameters from high-order ARX models. A recursive conversion between observer Markov parameters and system Markov parameters is developed under the non-stationary Kalman filter structure. The system Markov parameters can be obtained and further applied to disturbance modeling in model predictive control. Simulations are carried out to show the effect of the non-stationary Kalman filter parametrization with finite data. © 2012 AACC American Automatic Control Council).

Original language	English
Title of host publication	2012 American Control Conference (ACC)
Publisher	IEEE
Pages	4813-4818
ISBN (Electronic)	978-1-4577-1096-4
ISBN (Print)	978-1-4577-1095-7
DOIs	https://doi.org/10.1109/acc.2012.6315683
Publication status	Published - Jun 2012
Externally published	Yes
Event	2012 American Control Conference, ACC 2012 - Montreal, Canada Duration: 27 Jun 2012 → 29 Jun 2012 https://ieeexplore.ieee.org/xpl/conhome/6297579/proceeding

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Conference

Conference	2012 American Control Conference, ACC 2012
Place	Canada
City	Montreal
Period	27/06/12 → 29/06/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6297579/proceeding

Access Output

10.1109/acc.2012.6315683

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{62e917e6f5404a219a76eb35686f6f04,

title = "Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC",

abstract = "In this paper, a non-stationary Kalman filter parametrization of subspace identification models is adopted to deal with finite data windows. We show that the non-stationary Kalman filter parametrization is the solution to the least squares estimation of the Markov parameters from high-order ARX models. A recursive conversion between observer Markov parameters and system Markov parameters is developed under the non-stationary Kalman filter structure. The system Markov parameters can be obtained and further applied to disturbance modeling in model predictive control. Simulations are carried out to show the effect of the non-stationary Kalman filter parametrization with finite data. {\textcopyright} 2012 AACC American Automatic Control Council).",

author = "Yu Zhao and Zhijie Sun and Qin, \{S. Joe\} and Tianyou Chai",

year = "2012",

month = jun,

doi = "10.1109/acc.2012.6315683",

language = "English",

isbn = "978-1-4577-1095-7",

series = "Proceedings of the American Control Conference",

publisher = "IEEE",

pages = "4813--4818",

booktitle = "2012 American Control Conference (ACC)",

address = "United States",

note = "2012 American Control Conference, ACC 2012 ; Conference date: 27-06-2012 Through 29-06-2012",

url = "https://ieeexplore.ieee.org/xpl/conhome/6297579/proceeding",

}

Zhao, Y, Sun, Z, Qin, SJ & Chai, T 2012, Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC. in 2012 American Control Conference (ACC)., 6315683, Proceedings of the American Control Conference, IEEE, pp. 4813-4818, 2012 American Control Conference, ACC 2012, Montreal, Quebec, Canada, 27/06/12. https://doi.org/10.1109/acc.2012.6315683

Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC. / Zhao, Yu; Sun, Zhijie; Qin, S. Joe et al.
2012 American Control Conference (ACC). IEEE, 2012. p. 4813-4818 6315683 (Proceedings of the American Control Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC

AU - Zhao, Yu

AU - Sun, Zhijie

AU - Qin, S. Joe

AU - Chai, Tianyou

PY - 2012/6

Y1 - 2012/6

N2 - In this paper, a non-stationary Kalman filter parametrization of subspace identification models is adopted to deal with finite data windows. We show that the non-stationary Kalman filter parametrization is the solution to the least squares estimation of the Markov parameters from high-order ARX models. A recursive conversion between observer Markov parameters and system Markov parameters is developed under the non-stationary Kalman filter structure. The system Markov parameters can be obtained and further applied to disturbance modeling in model predictive control. Simulations are carried out to show the effect of the non-stationary Kalman filter parametrization with finite data. © 2012 AACC American Automatic Control Council).

AB - In this paper, a non-stationary Kalman filter parametrization of subspace identification models is adopted to deal with finite data windows. We show that the non-stationary Kalman filter parametrization is the solution to the least squares estimation of the Markov parameters from high-order ARX models. A recursive conversion between observer Markov parameters and system Markov parameters is developed under the non-stationary Kalman filter structure. The system Markov parameters can be obtained and further applied to disturbance modeling in model predictive control. Simulations are carried out to show the effect of the non-stationary Kalman filter parametrization with finite data. © 2012 AACC American Automatic Control Council).

UR - https://www.scopus.com/pages/publications/84869483688

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84869483688&origin=recordpage

U2 - 10.1109/acc.2012.6315683

DO - 10.1109/acc.2012.6315683

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-4577-1095-7

T3 - Proceedings of the American Control Conference

SP - 4813

EP - 4818

BT - 2012 American Control Conference (ACC)

PB - IEEE

T2 - 2012 American Control Conference, ACC 2012

Y2 - 27 June 2012 through 29 June 2012

ER -

Non-stationary Kalman Filter Parametrization of Subspace Models with Applications to MPC

Abstract

Publication series

Conference

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this