Recursive subspace identification of linear and non-linear Wiener state-space models

Automatica

Volumn 36, Issue 11, 2000, Pages 1639-1650

  Lovera, Marco ^a   Gustafsson, Tony ^b   Verhaegen, Michel ^c  

^a POLITECNICO DI MILANO   (Italy)

^b CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

^c UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; RECURSIVE FUNCTIONS; SIGNAL PROCESSING; STATE SPACE METHODS;

INSTRUMENTAL VARIABLE (IV) APPROACH; MULTIVARIABLE OUTPUT-ERROR STATE SPACE (MOESP); RECURSIVE ALGORITHM; SINGULAR VALUE DECOMPOSITION (SVD); SUBSPACE MODEL IDENTIFICATION (SMI); WIENER MODELS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0034326121     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0005-1098(00)00103-5     Document Type: Article

References (26)

1. Abdelghani, M., & Verhaegen, M. (1995). Structural model parameter estimation using subspace methods. In New advances in modal synthesis of large structures, Lyon, France.
2. Cho Y., Kailath T. Fast subspace-based identification: An instrumental variable approach. Automatica. 31(6):1995;903-905.
3. Chou C.T., Verhaegen M. Subspace algorithms for the identification of multivariable dynamic error-in-variables state space models. Automatica. 33(10):1997;1857-1869.
4. Comon P., Golub G. Tracking a few extreme singular values and vectors in signal processing. Proceedings of the IEEE. 78(8):1990;1327-1343.
5. Golub G., Van Loan C. Matrix computations. 1989;John Hopkins University Press, Baltimore, MD.
6. Gustafsson, T. (1997a). Recursive system identification using instrumental variable subspace tracking. Proceedings of the 11th IFAC Symposium on System Identification, Fukuoka, Japan.
7. Gustafsson, T. (1997b). System identification using subspace-based instrumental variable methods. Proceedings of the 11th IFAC Symposium on System Identification, Fukuoka, Japan.
8. Gustafsson, T. (1997c). Instrumental variable subspace tracking using projection approximation. IEEE Transactions on Signal Processing, 46(3) 669-681.
9. Haverkamp, B., & Verhaegen, M. (1997). SMI Toolbox: State space model identification software for multivariable dynamical systems, v. 0.9. Delft University of Technology, http: // lcewww.et.tudelft.nl/haver/smi.html.
10. Jansson, M. (1997). On subspace methods in system identification and sensor array signal processing. Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden.
11. Ljung L. System identification. 1987;Prentice-Hall, Englewood Cliffs, NJ.
12. Lovera, M. (1998). Subspace identification methods: Theory and applications Ph.D. thesis, Politecnico di Milano.
13. Lovera, M., & Verhaegen, M. (1998). Recursive subspace identification of linear and non-linear Wiener type models. IEEE CCA'98, Trieste, Italy.
14. McKelvey, T. (1994). Identification of state-space models from time and frequency data. Ph.D. thesis, Department of Electrical Engineering, Linkoping University.
15. Van Overschee P., De Moor B. Subspace identification: theory, implementation, application. 1996;Kluwer Academic Publishers, Dordrecht.
16. Verhaegen M. Subspace model identification, Part 3: Analysis of the ordinary output error state space model identification algorithm. International Journal of Control. 58(3):1993;555-586.
17. Verhaegen M. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica. 30(1):1994;61-74.
18. Verhaegen, M. (1998). Accurate identification of the temperature-product quality relationship in a multi-component distillation column. Chemical Engineering Communications, Invited paper, 163, 111-132.
19. Verhaegen, M., & Deprettere, E. (1991). A fast, recursive mimo state space model identification algorithm. Proceedings of the 1991 Conference on Decision and Control (pp. 1349-1354).
20. Verhaegen M., Dewilde P. Subspace model identification, Part 1: Output error state space model identification class of algorithms. International Journal of Control. 56(5):1992;1187-1210.
21. Viberg M. Subspace-based methods for the identification of linear time-invariant systems. Automatica. 31(3):1995;1835-1852.
22. Westwick, D., & Verhaegen, M. (1996). Identifying mimo Wiener systems using subspace model identification. Signal Processing, 52, Invited paper for special issue (pp 235-258).
23. Wigren T. Recursive prediction error identification using the nonlinear Wiener model. Automatica. 29(4):1993;1011-1025.
24. Wigren T. Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model. IEEE Trans. on Automatic Control. 39(11):1994;2191-2205.
25. Yang B. Projection approximation subspace tracking. IEEE Transactions on Signal Processing. 43(1):1995;95-107.
26. Yang B. Asymptotic convergence analysis of the projection approximation subspace tracking algorithm. Signal Processing. 50(1):1996;123-136.