The role of vector autoregressive modeling in predictor-based subspace identification

Automatica

Volumn 43, Issue 6, 2007, Pages 1034-1048

  Chiuso, Alessandro ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Asymptotic properties; Closed loop identification; Relative efficiency; Subspace methods; Variance

Indexed keywords

ALGORITHMS; ASYMPTOTIC ANALYSIS; COMPUTATIONAL COMPLEXITY; ERROR ANALYSIS; REGRESSION ANALYSIS; VECTORS;

ASYMPTOTIC PROPERTIES; CLOSED LOOP IDENTIFICATION; PREDICTION ERROR METHODS (PEM); RELATIVE EFFICIENCY; SUBSPACE METHODS; VARIANCE;

CLOSED LOOP SYSTEMS;

EID: 34247573113     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2006.12.009     Document Type: Article

References (53)

1. Bauer D. Order estimation for subspace methods. Automatica 37 (2001) 1561-1573
2. Bauer D. Asymptotic properties of subspace estimators. Automatica 41 (2005) 359-376
3. Bauer D. Comparing the CCA subspace method to pseudo maximum likelihood methods in the case of no exogenous inputs. Journal of Time Series Analysis 26 (2005) 631-668
4. Bauer D., and Jansson M. Analysis of the asymptotic properties of the MOESP type of subspace algorithms. Automatica 36 (2000) 497-509
5. Bauer D., and Ljung L. Some facts about the choice of the weighting matrices in Larimore type of subspace algorithm. Automatica 38 (2002) 763-773
6. Chiuso, A. (2007a). Some insights on the choice of the future horizon in CCA-type subspace algortihms, Proc. of the ACC 2007, to appear, available at 〈http://www.dei.unipd.it/∼chiuso〉.
7. Chiuso, A. (2007b). On the relation between CCA and predictor based subspace identification, IEEE Transactions on Automatic Control, to appear, available at 〈http://www.dei.unipd.it/∼chiuso〉.
8. Chiuso A. Asymptotic equivalence of certain closed-loop subspace identification methods. Proceedings of SYSID, 2006 (2006), Newcastle, Australia
9. Chiuso A. Asymptotic variance of closed-loop subspace identification algorithms. IEEE Transactions on Automatic Control 51 8 (2006) 1299-1314
10. Chiuso, A. (2006c). The role of vector autoregressive modeling in subspace identification. Proceedings of CDC 2006, San Diego, USA, December 2006.
11. Chiuso, A.,& Picci, G. (2003a). Constructing the state of random processes with feedback. Proceedings of the IFAC international symposium on system identification (SYSID), Rotterdam, August 2003.
12. Chiuso, A., & Picci, G. (2003b). Geometry of oblique splitting, minimality and hankel operators. Lecture notes in control and information sciences, (Vol. 286, pp. 85-124). Berlin: Springer.
13. Chiuso A., and Picci G. The asymptotic variance of subspace estimates. Journal of Econometrics 118 1-2 (2004) 257-291
14. Chiuso A., and Picci G. Asymptotic variance of subspace methods by data orthogonalization and model decoupling: A comparative analysis. Automatica 40 10 (2004) 1705-1717
15. Chiuso A., and Picci G. On the ill-conditioning of subspace identification with inputs. Automatica 40 4 (2004) 575-589
16. Chiuso A., and Picci G. Consistency analysis of some closed-loop subspace identification methods. Automatica 41 3 (2005) 377-391
17. Chiuso, A., & Picci, G. (2005b). Prediction error vs. subspace methods in closed-loop identification, Proceedings of the 16th IFAC world congress, Prague, July 2005.
18. Dahlén A., and Scherrer W. The relation of CCA subspace method to a balanced reduction of an autoregressive model. Journal of Econometrics 118 1-2 (2004) 293-312
19. Ferguson T. A course in large sample theory (1996), Chapman and Hall, London
20. Golub G.H., and Van Loan C.R. Matrix computation. 2nd ed. (1989), The Johns Hopkins University Press, Baltimore, MD
21. Granger C.W.J. Economic processes involving feedback. Information and Control 6 (1963) 28-48
22. Hannan E.J., and Deistler M. The statistical theory of linear systems (1988), Wiley, New York
23. Hong, Z., Harmse, M., Guiver, J., & Canney, W. (2006). Subspace identification in industrial MPC applications-a review of recent progress and industrial experience (i). Proceedings of SYSID 2006, Newcastle, Australia.
24. Jansson M. Asymptotic variance analysis of subspace identification methods. Proceedings of SYSID 2000 (2000), Santa Barbara, CA
25. Jansson, M. (2003). Subspace identification and ARX modeling. Proceedings of SYSID 2003, Rotterdam.
26. Jansson, M. (2005). A new subspace identification method for open and closed loop data. Proceedings of the 16th IFAC World Congress, Prague.
27. Katayama T., Kawauchi H., and Picci G. Subspace identification of closed loop systems by orthogonal decomposition. Automatica 41 (2005) 863-872
28. Katayama, T., Tanaka, H., & Enomoto, T. (2005). A simple subspace identification method of closed-loop systems using orthogonal decomposition. Proceedings of the 16th IFAC world congress, Prague.
29. Kuersteiner G.M. Automatic inference for infinite order vector autoregressions. Econometric Theory 21 (2005) 85-115
30. Larimore, W.E. (1983). System identification, reduced-order filtering and modeling via canonical variate analysis. Proceedings of the American control conference (pp. 445-451).
31. Larimore W.E. Large sample efficiency for ADAPTX subspace identification with unknown feedback. Proceedings of IFAC DYCOPS'04 (2004), Boston, MA, USA
32. Lindquist A., and Picci G. Canonical correlation analysis, approximate covariance extension and identification of stationary time series. Automatica 32 (1996) 709-733
33. Ljung L. System identification, theory for the user (1997), Prentice Hall, Englewood Chiffs, NJ
34. Ljung, L., & McKelvey, T. (1995). Subspace identification from closed loop data. Technical report LiTH-ISY-R-1752.
35. Ljung L., and McKelvey T. Subspace identification from closed loop data. Signal Processing 52 2 (1996) 209-216
36. Onodera, K., Emoto, G., & Qin, S.J. (2006). A new subspace identification method for closed loop systems. Proceedings of SYSID 2006, Newcastle, Australia.
37. Peternell, K. (1995). Subspace methods for subspace identification, Ph.D. thesis, Technical University of Vienna.
38. Peternell K., Scherrer W., and Deistler M. Statistical analysis of novel subspace identification methods. Signal Processing 52 (1996) 161-178
39. Porat, B., & Friedlander, B. (1985). Asymptotic accuracy of ARMA parameter estimation methods based on sample covariances. Proceedings of the seventh IFAC SYSID, York.
40. Qin, S.J., & Ljung, L. (2003a). Closed-loop subspace identification with innovation estimation. Proceedings of SYSID 2003, Rotterdam.
41. Qin, S.J., & Ljung, L. (2003b). Parallel QR implementation of subspace identification with parsimonious models. Proceedings of SYSID 2003, Rotterdam.
42. Qin, S.J., & Ljung, L. (2006). On the role of future horizon in closed-loop subspace identification. Proceedings of SYSID 2006, Newcastle, Australia.
43. Rao C.R. Representations of the best linear unbiased estimators in the Gauss-Markov model with a singular dispersion matrix. Journal of Multivariate Analysis 3 (1973) 276-292
44. Shi, F., & MacGregor, J.F. (2001). A framework for subspace identification. Proceedings of IEEE ACC, Arlington, VA.
45. Söderström T., and Stoica P. System identification (1989), Prentice-Hall, Englewood Cliffs, NJ
46. Van Overschee P., and De Moor B. Subspace algorithms for the stochastic identification problem. Automatica 29 (1993) 649-660
47. Van Overschee P., and De Moor B. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30 (1994) 75-93
48. Van Overschee, P., & De Moor, B. (1997). Closed loop subspace systems identification. Proceedings of 36th IEEE conference on decision and control, San Diego, CA (pp. 1848-1853).
49. Verhaegen M. Application of a subspace model identification technique to identify lti systems operating in closed-loop. Automatica 29 (1993) 1027-1040
50. Wahlberg B. Estimation of autoregressive moving-average models via high order autoregressive approximations. Journal of Time Series Analysis 10 3 (1989) 283-299
51. Walker A.M. Large-sample estimation of parameters for moving average models. Biometrika 48 3/4 (1961) 343-357
52. Werner H.J., and Yapar C. On inequality constrained generalized least squares selections in the general possibly singular Gauss-Markov model: A projector theoretical approach. Linear Algebra and its Applications 237/238 1-3 (1996) 359-393 (Special issue honoring Calyampudi Radhakrishna Rao)
53. Zhu Y. System identification for process control: Recent experiences and outlook (2006), Plenary Lecture delivered at SYSID 2006, Newcastle, Australia