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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.8 P.1177-1186


Subspace identification for continuous-time errors-in-variables model from sampled data

Author(s):  Ping WU, Chun-jie YANG, Zhi-huan SONG

Affiliation(s):  State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   pwu@iipc.zju.edu.cn, cjyang@iipc.zju.edu.cn

Key Words:  System identification, Errors-in-variables, Continuous-time system, Subspace method

Ping WU, Chun-jie YANG, Zhi-huan SONG. Subspace identification for continuous-time errors-in-variables model from sampled data[J]. Journal of Zhejiang University Science A, 2009, 10(8): 1177-1186.

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%T Subspace identification for continuous-time errors-in-variables model from sampled data
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%A Zhi-huan SONG
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820684

T1 - Subspace identification for continuous-time errors-in-variables model from sampled data
A1 - Ping WU
A1 - Chun-jie YANG
A1 - Zhi-huan SONG
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 8
SP - 1177
EP - 1186
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820684

We study the subspace identification for the continuous-time errors-in-variables model from sampled data. First, the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification. The generalized Poisson moment functional is focused. A total least squares equation based on this filtering approach is derived. Inspired by the idea of discrete-time subspace identification based on principal component analysis, we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables. Order determination and other instrumental variables are discussed. The usefulness of the proposed algorithms is illustrated through numerical simulation.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Bastogne, T., Garnier, H., Sibille, P., 2001. A PMF-based subspace method for continuous-time model identification. Application to a multivariable winding process. Int. J. Control, 74(2):118-132.

[2] Bauer, D., 2005. Asymptotic properties of subspace estimators. Automatica, 41(3):359-376.

[3] Garnier, H., Wang, L., 2008. Identification of Continuous-time Models from Sampled Data. Springer Verlag, London.

[4] Garnier, H., Mensler, M., Richard, A., 2003. Continuous-time model identification from sampled data: implementation issues and performance evaluation. Int. J. Control, 76(13):1337-1357.

[5] Garnier, H., Gilson, M., Cervellin, O., 2006. Latest Developments for the Matlab CONTSID Toolbox. 14th IFAC Symp. on System Identification, p.174-179.

[6] Haverkamp, B., 2001. State Space Identification: Theory and Practice. PhD Thesis, Delft University of Technology, The Netherlands.

[7] Huang, B., Ding, S.X., Qin, S.J., 2005. Closed-loop subspace identification: an orthogonal projection approach. J. Process Control, 15(1):53-66.

[8] Johansson, R., Verhaegen, M., Chou, C., 1999. Stochastic theory of continuous time state space identification. IEEE Trans. Signal Process., 47(1):41-51.

[9] Larimore, W.E., 1990. Canonical Variate Analysis in Identification, Filtering and Adaptive Control. 29th IEEE Conf. on Decision and Control, p.596-604.

[10] Li, W., Raghavan, H., Shah, S., 2003. Subspace identification of continuous time models for process fault detection and isolation. J. Process Control, 13(5):407-421.

[11] Li, W.H., Qin, S.J., 2001. Consistent dynamic PCA based on errors-in-variables subspace identification. J. Process Control, 11(6):661-678.

[12] Mahata, K., Garnier, H., 2006. Identification of continuous-time errors-in-variables models. Automatica, 49(9):1470-1490.

[13] Mercère, G., Ouvrard, R., Gilson, M., Garnier, H., 2007. Subspace Based Methods for Continuous-time Model Identification of MIMO Systems from Filtered Sampled Data. European Control Conf., CDROM.

[14] Ohsumi, A., Kameyama, K., Yamagushi, K., 2002. Subspace identification for continuous time stochastic systems via distribution based approach. Automatica, 38(1):63-79.

[15] Sinha, N.K., Rao, G.P. (Eds.), 1991. Identification of Continuous-time Systems: Methodology and Computer Implementation. Kluwer Academic Publishers, Dordrecht.

[16] Söderström, T., 2007. Errors-in-variables methods in system identification. Automatica, 43(6):939-958.

[17] Sung, S.W., Lee, S.Y., Kwak, H.J., Lee, I.B., 2001. Continuous-time subspace system identification method. Ind. Eng. Chem. Res., 40(13):2886-2896.

[18] Thil, S., Garniera, H., Gilsona, M., 2008. Third-order cumulants based methods for continuous-time errors-in-variables model identification. Automatica, 44(3):647-658.

[19] Unbehauen, H., Rao, G.P., 1987. Identification of Continuous Systems. North-Holland, Amsterdam.

[20] Unbehauen, H., Rao, G.P., 1990. Continuous-time approaches to system identification—a survey. Automatica, 26(1):23-35.

[21] van Huffel, S., Lemmerling, P., 2002. Errors in Variables Modelling: Analysis, Algorithms and Applications. Kluwer Academic Publishers, Philadelphia.

[22] van Overschee, P., de Moor, B., 1994. N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30(1):75-93.

[23] Verhaegen, M., Dewilde, P., 1992. Subspace model identification, part I: the output-error state-space model identification class of algorithms. Int. J. Control, 56(5):1187-1210.

[24] Wang, J., Qin, S.J., 2002. A new subspace identification approach based on principal component analysis. J. Process Control, 12(8):841-855.

[25] Wang, J., Qin, S.J., 2006. Closed-loop subspace identification using the parity space. Automatica, 42(2):315-320.

[26] Wang, L., Gawthrop, P., Chessari, C., Podsiadly, T., Giles, A., 2004. Indirect approach to continuous time system identification of food extruder. J. Process Control, 14(6):603-615.

[27] Young, P.C., Garnier, H., 2006. Identification and estimation of continuous-time data-based mechanistic (DBM) models for environmental systems. Environ. Model. Software, 21(8):1055-1072.

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