State estimation for large-scale systems based on reduced-order error-covariance propagation

Proceedings of the American Control Conference

Volumn , Issue , 2007, Pages 5700-5705

  Kim, I S ^a   Chandrasekar, J ^a   Palanthandalam Madapusi, H J ^a   Ridley, A J ^a   Bernstein, D S ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

(ALGORITHMIC) COMPLEXITY; COMPARTMENTAL MODELLING; DISCRETE TIME (DT); LARGE-SCALE DATA; LARGE-SCALE SYSTEMS; LINEAR TIME-INVARIANCE (LTI) SYSTEMS; NUMERICAL STUDIES; REDUCED ORDER;

COMPUTATIONAL COMPLEXITY; CONTROL THEORY; CRACK PROPAGATION; MILITARY DATA PROCESSING; NUMERICAL ANALYSIS;

WAVE FILTERS;

EID: 46449130575     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.2007.4282477     Document Type: Conference Paper

References (25)

1. A. Azemi, E. Yaz, and K. Olejniczak. Reduced-order estimation of power system harmonics. In Proceedings of the 4th IEEE Conference on Control Applications, pages 631-636, Sept. 1995.
2. M. Barut, O. S. Bogosyan, and M. Gokasan. An EKF based reduced order estimator for the sensorless control of IMS. In Proceedings of 2003 IEEE Conference on Control Applications, 2003, volume 2, pages 1256-1261, June 2003.
3. D. S. Bernstein and D. C. Hyland. The optimal projection equations for reduced-order state estimation. IEEE Trans. Autom. Contr., AC-30:583-585, 1985.
4. W. M. Haddad and D. S. Bernstein Optimal reduced-order observerestimators. AIAA J. Guid. Contr. Dyn., 13:1126-1135, 1990.
5. K. W. Corscadden and S.R. Duncan. Reduced-order estimator for closed-loop online estimation of cross-directional parameters in a plastics extrusion process. IEE Proceedings of Control Theory and Applications, 144(6):549-557, 1997.
6. D. Haessig and B. Friedland. Separate-bias estimation with reduced-order Kalman filters. IEEE Transactions on Automatic Control, 43(7):983-987, 1998.
7. D. A. Haessig and B. Friedland. Nonlinear reduced-order state and parameter observer. In Proceedings of the 2001 American Control Conference, volume 3, pages 1978-1980, June 2001.
8. Y. Halevi. The optimal reduced-order estimator for systems with singular measurement noise. IEEE Transactions on Automatic Control, 34(7):777-781, 1989.
9. P. Hippe and C. Wurmthaler. Optimal reduced-order estimators in the frequency domain: The discrete-time case. Int. J. Contr., 52:1051-1064, 1990.
10. H. S. Hoang, R. Baraille, and O. Talagrand. On the design of a stable adaptive filter for state estimation in high dimensional systems. Automatica, 37(3):341-359, 2001.
11. C. S. Hsieh. The unified structure of unbiased minimum-variance reduced-order filters. In Contr. Dec. Conf., pages 4871-4876, Maui, HI, Dec. 2003.
12. C. S. Hsieh and F. C. Chen. Optimal minimal-order least-squares estimators via the general two-stage Kalman filter. IEEE Transactions on Automatic Control, 46(11):1772-1776, 2001.
13. C. T. Leondes and L. M. Novak. Optimal minimal-order observers for discrete-time systems-a unified theory. Automatica, 8(4):379-387, 1972.
14. R.P. Paroda and A. Kumar. Optimal reduced-order estimator design for discrete, time-varying systems. In IEEE International Conference on Systems Engineering, pages 367-370, Aug. 1991.
15. S. Roy and P. Mookerjee. Hierarchical estimation with reduced order local observers. In Proceedings of the 28th IEEE Conference on Decision and Control, volume 1, pages 835-836, Dec. 1989.
16. B. F. Farrel and P. J. Ioannou. State estimation using a reduced-order Kalman filter. J. of the Atmospheric Sciences, 58:3666-3680, 2001.
17. B. F. Farrell and P. J. Ioannou. Predictability of weather and climate. chapter 8, pages 181-216. Cambridge University Press, July 2006.
18. A. W. Heemink, M. Verlaan, and A. J. Segers. Variance-reduced ensemble kalman filtering. Mon. Weather Rev., 129:1718-1728, 2001.
19. A. J. Busalacchi J. Ballabrera-Poy and R. Murtugudde. Application of a reduced-order kalman filter to initialize a coupled atmosphere-ocean model: Impact on the prediction of el nino. J. Climate, 14:1720-1737, 2001.
20. D. Menemenlis P. Fieguth and I. Fukumori. Mapping and pseudo-inverse algorithms for data assimilation. In Int. Geoscience Remote Sensing Symp., pages 3221-3223, 2002.
21. A. Segers. Data Assimilation in atmospheric chemistry models using Kalman filtering. DUP Science, The Netherlands, 2002.
22. J. M. Lewis, S. Lakshmivarahan, and Sudarshan Dhall. Dynamic Data Assimilation: A Least Squares Approach. Cambridge University Press, 2006.
23. A. C. Antoulas. Approximation of Large-Scale Dynamical Systems. SIAM, Society for Industrial and Applied Mathematics, 2005.
24. J. Chandrasekar, I. S. Kim, A. J. Ridley, and D. S. Bernstein. A localized unscented Kalman filter based on reduced-order-error-covariance propagation and complementary steady-state correlation. In Amer. Contr. Conf., New York, NY, June 2007.
25. D. S. Bernstein and D. C. Hyland. Compartmental modeling and second-moment analysis of state space systems. SIAM J. Matrix Anal. Appl., 14(3):880-901, 1993.