Subspace identification - A Markov parameter approach

International Journal of Control

Volumn 78, Issue 17, 2005, Pages 1412-1436

  Chui, N L C ^a   Maciejowski, J M ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BALANCED MODELS; BLOCK ARRANGEMENT; CENTRAL COMPONENT; DATA MATRICES; INDUSTRIAL SYSTEMS; MARKOV PARAMETERS; STATE SEQUENCES; SUB-SPACE ALGORITHMS; SUBSPACE IDENTIFICATION; SUBSPACE IDENTIFICATION METHODS; SYSTEM MATRICES;

ALGORITHMS; MATRIX ALGEBRA;

PARAMETER ESTIMATION;

EID: 47249109288     PISSN: 00207179     EISSN: 13665820     Source Type: Journal    
DOI: 10.1080/00207170500362001     Document Type: Article

References (32)

1. H. Akaike, "Stochastic theory of minimal realization", IEEE Trans. on Automatic Control, 19, pp. 667-674, 1974.
2. D. Bauer, "Some asymptotic theory for the estimation of linear systems using maximum likelihood methods or subspace algorithms", PhD thesis, Technical University of Vienna, Austria (1998).
3. C. Chou, "Geometry of Linear Systems and Identification", PhD thesis, Cambridge University, UK (1994).
4. C. Chou and J. Maciejowski, "System identification using balanced parameterizations", IEEE Trans. on Automatic Control, 42, pp. 956-974, 1997.
5. N. Chui, "Subspace Methods and Informative Experiments for System Identification", PhD thesis, Cambridge University, U.K. Also available at URL: http://wwwcontrol.eng.cam.ac.uk/nlcc/report/thesis.ps (1997).
6. N. Chui and J. Maciejowski, "Realization of stable models with subspace methods", Automatica, 32, pp. 1587-1595, 1996a.
7. N. Chui and J. Maciejowski, "Uniqueness of minimal partial realizations and Markov parameter identification", in 35th IEEE Conf. on Decision and Control, pages 3642-3647, Kobe, 1996b.
8. N. Chui and J. Maciejowski, "Criteria for informative experiments with subspace identification", International Journal of Control, 78, pp. 326-344, 2005.
9. N. Chui and J. Maciejowski, A geometric interpretation for subspace identification. Technical Report CUED/F-INFENG/TR.339, Cambridge University, U.K. Also available at URL: "http:// wwwcontrol.eng.cam.ac.uk/jmm/papers/ TR339.ps", 1998b.
10. N. Chui and J. Maciejowski, Subspace identification algorithm a Markov parameter approach. Technical Report CUED/ F-INFENG/TR.337, Cambridge University, U.K. 1998c.
11. N. Chui and J. Maciejowski, "An unbiased subspace algorithm with the state sequence approach", in Mathematical Theory on Networks and Systems, A. Beghi, L. Finesso and G. Picci, Eds, Padova, Italy. Il Poligrafo, pp. 731-734, 1999.
12. B. De Moor, DAISY: Database for the Identification of Systems, Department of Electrical Engineering, ESAT/SISTA, K.U.Leuven, Belgium, URL: http://www.esat.kuleuven.ac.be/sista/daisy/, 1997.
13. M. Deistler, K. Peternell and W. Scherrer, "Consistency and relative efficiency of subspace methods", Automatica, 31, pp. 1865-1875, 1995.
14. R. Horn and C. Johnson, Topics in Matrix Analysis, Cambridge, UK: Cambridge University Press, 1994.
15. M. Jansson and B. Wahlberg, "On weighting in statespace subspace system identification", in 3rd European Control Conf., Vol.1, pp. 435-440, Rome, Italy, 1995.
16. M. Jansson and B. Wahlberg, "A linear regression approach to state-space subspace system identification", Signal Processing, 52, pp. 103-129, 1996.
17. R. Kalman, "On minimal partial realizations of a linear input/ output map", in Aspects of Network and System Theory, R. Kalman and N. DeClaris, Eds, New York: Holt, Reinhart and Winston, 1971.
18. S. Kung, "A new low-order approximation algorithm via singular value decomposition", in 12th Asilomar Conf. on Circuits, Systems and Computers, pp. 705-714, Asilomar, 1978.
19. W. Larimore, "Canonical variate analysis in identification, filtering, and adaptive control", in 29th IEEE Conf. on Decision and Control, pp. 596-604, Honolulu, Hawaii, 1990. (Pubitemid 21691470)
20. L. Ljung, System Identification - Theory for the Users, N.J., USA: Prentice-Hall, Inc., 1987.
21. M. Moonen, B. De Moor, L. Vandenberghe and J. Vandewalle, "On-and off-line identification of linear state-space models", Int. J. Control, 49, pp. 219-232, 1989.
22. A. Nerode, "Linear automaton transformations", Proc. Amer. Math. Soc., 9, pp. 541-544, 1958.
23. K. Peternell, W. Scherrer and M. Deistler, "Statistical analysis of novel subspace identification methods", Signal Processing, 52, pp. 161-177, 1996.
24. P. Van Overschee and B. De Moor, "Subspace algorithms for the stochastic identification problem", Automatica, 29, pp. 649-660, Dordrecht, 1993.
25. P. Van Overschee and B. De Moor, "N4SID: Subspace algorithms for the identification of combined deterministic stochastic systems", Automatica, 30, pp. 75-93, 1994.
26. P. Van Overschee and B. De Moor, "A unifying theorem for three subspace system-identification algorithms", Automatica, 31, pp. 1853-1864, 1995.
27. P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, The Netherlands: Kluwer Academic Publishers, 1996.
28. M. Verhaegen, "Identification of the deterministic part of MIMO state space models given in innovations form from input-output data", Automatica, 30, pp. 61-74, 1994.
29. M. Verhaegen and P. Dewilde, "Subspace model identification - Part 1, Int. J. Control, 56, pp. 1187-1210, 1992.
30. M. Viberg, "Subspace-based methods for the identification of linear time-invariant systems", Automatica, 31, pp. 1835-1851, 1995.
31. M. Viberg, B. Wahlberg and B. Ottersten, "Analysis of state space system identification methods based on instrumental variables and subspace fitting", Automatica, 33, pp. 1603-1616, 1997.
32. B. Wahlberg and M. Jansson, "4SID linear regression", in 33rd IEEE Conf. on Decision and Control, pp. 2858-2863, Florida, 1994.