A novel subspace identification approach with enforced causal models

Automatica

Volumn 41, Issue 12, 2005, Pages 2043-2053

  Qin, S Joe ^a   Lin, Weilu ^a,c   Ljung, Lennart ^{b,d,e,f,g,h,i,j,k}  

^a The University of Texas at Austin   (United States)

^b LINKÖPING UNIVERSITY   (Sweden)

^c WEYERHAEUSER COMPANY   (United States)

^d Department of Automatic Control ^*  (Sweden)

^e Competence Center Information Systems for Industrial Control and Supervision ^*

^f IEEE

^g IFAC   (Italy)

^h ROYAL SWEDISH ACADEMY OF SCIENCES   (Sweden)

ⁱ Royal Swedish Academy of Engineering Sciences (IVA)   (Sweden)

^j Hungarian Academy of Engineering   (Hungary)

^k NATIONAL ACADEMY OF ENGINEERING   (United States)

more..

Author keywords

Causal model; Subspace identification; Variance analysis

Indexed keywords

CAUSAL MODEL; STATE-SPACE MODELS; SUBSPACE IDENTIFICATION; VARIANCE ANALYSIS;

CLOSED LOOP CONTROL SYSTEMS; COMPUTER SIMULATION; MATHEMATICAL MODELS; NUMERICAL ANALYSIS; PROBLEM SOLVING; VARIATIONAL TECHNIQUES;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 27344443753     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2005.06.010     Document Type: Article

References (33)

1. Bauer, D. (2003). Subspace algorithms. In Proceedings of the 13th IFAC SYSID symposium (pp. 1030-1041). Rotterdam, Netherlands, August.
2. D. Bauer Asymptotic properties of subspace estimators Automatica 41 2005 359 376
3. D. Bauer, and M. Jansson Analysis of the asymptotic properties of the MOESP type of subspace algorithm Automatica 36 2000 497 509
4. D. Bauer, and L. Ljung Some facts about the choice of the weighting matrices in LARIMORE type of subspace algorithms Automatica 38 2002 763 773
5. A. Chiuso, and G. Picci The asymptotic variance of subspace estimates Journal of Econometrics 118 2004 257 291
6. C.T. Chou, and M. Verhaegen Subspace algorithms for the identification of multivariable dynamic errors-in-variables models Automatica 33 1997 1857 1869
7. D. Di Ruscio Combined deterministic and stochastic system identification and realization: DSR - a subspace approach based on observations Modeling, Identification and Control 17 1996 193 230
8. T. Gustafsson Subspace-based system identification: Weighting and pre-filtering of instruments Automatica 38 2002 433 443
9. T. Gustafsson, and B.D. Rao Statistical analysis of subspace-based estimation of reduced-rank linear regression IEEE Transactions of Signal Processing 50 2002 151 159
10. Jansson, M. (2003). Subspace identification and ARX modelling. In Proceedings of the 13th IFAC SYSID symposium, Rotterdam, Netherlands, August.
11. M. Jansson, and B. Wahlberg A linear regression approach to state-space sub-space system identification Signal Processing 52 1996 103 129
12. M. Jansson, and B. Wahlberg On consistency of subspace methods for system identification Automatica 34 1998 1507 1519
13. T. Knudsen Consistency analysis of subspace identification methods based on linear regression approach Automatica 37 2001 81 89
14. Larimore, W. E. (1990). Canonical variate analysis in identification, filtering and adaptive control. In IEEE conference on decision and control (pp. 596-604), December.
15. Larimore, W. E. (1992). ADAPTx Automated System Identification Software Users Manual. Adaptics, Inc.
16. W.E. Larimore Statistical optimality and canonical variate analysis system identification Signal Processing 52 1996 131 144
17. Larimore, W. E. (2004). Large sample efficiency for ADAPTx subspace system identification with unknown feedback. In Proceedings of the 7th international symposium on DYCOPS, Boston, MA, July.
18. Ljung, L. (1999a). Estimation focus in system identification: Prefiltering, noise models, and prediction. In IEEE conference on decision and control (pp. 2810-2815), December.
19. L. Ljung System identification - theory for the user 2nd ed. 1999 Prentice Hall PTR Englewood Cliffs, NJ
20. Ljung, L. (2003). Aspects and experiences of user choices in subspace identification methods. In Proceedings of the 13th IFAC SYSID symposium (pp. 1802-1807). Rotterdam, Netherlands, August.
21. L. Ljung, and T. McKelvey Subspace identification from closed loop data Signal Processing 52 1996 209 215
22. K. Mardia, J. Kent, and J. Bibby Multivariate analysis 1979 Academic Press New York
23. K. Peternell, W. Scherrer, and M. Deistler Statistical analysis of novel subspace identification methods Signal Processing 52 1996 161 177
24. Qin, S., & Ljung, L. (2003a). Closed-loop subspace identification with innovation estimation. In Proceedings of the 13th IFAC SYSID symposium (pp. 887-892). Rotterdam, Netherlands, August.
25. Qin, S., & Ljung, L. (2003b). Parallel QR implementation of subspace identification with parsimonious models. In Proceedings of the 13th IFAC SYSID symposium (pp. 1631-1636). Rotterdam, Netherlands, August.
26. Shi, R. (2001). Subspace identification methods for dynamic process modeling. Ph.D. thesis, Department of Chemical Engineering, McMaster University.
27. Shi, R., & Macgregor, J. F. (2001). A framework for subspace identification methods. In Proceedings of the American control conference (pp. 3678-3683). Arlington, VA, June.
28. P. Van Overschee, and B. De Moor N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems Automatica 30 1994 75 93
29. P. Van Overschee, and B. De Moor A unifying theorem for three subspace system identification algorithms Automatica 31 1995 1853 1864
30. P. Van Overschee, and B. De Moor Subspace identification for linear systems 1996 Kluwer Academic Publishers Dordrecht
31. M. Verhaegen Identification of the deterministic part of MIMO state space models given in innovations form from input-output data Automatica 30 1994 61 74
32. M. Verhaegen, and P. Dewilde Subspace model identification. Part I: The output-error state-space model identification class of algorithms International Journal of Control 56 1992 1187 1210
33. J. Wang, and S.J. Qin A new subspace identification approach based on principal component analysis Journal of Process Control 12 2002 841 855