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“Closed Loop Subspace Identification”

Authors: Geir W. Nilsen and David Di Ruscio,
Affiliation: Telemark University College
Reference: 2005, Vol 26, No 3, pp. 151-164.

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Keywords: Closed loop, subspace system identification, Kalman filter, unbiased estimates

Abstract: A new three step closed loop subspace identifications algorithm based on an already existing algorithm and the Kalman filter properties is presented. The Kalman filter contains noise free states which implies that the states and innovation are uneorre lated. The idea is that a Kalman filter found by a good subspace identification algorithm will give an output which is sufficiently uncorrelated with the noise on the output of the actual process. Using feedback from the output of the estimated Kalman filter in the closed loop system a subspace identification algorithm can be used to estimate an unbiased model.

PDF PDF (1518 Kb)        DOI: 10.4173/mic.2005.3.3



DOI forward links to this article:
  [1] Gabriele Pannocchia and Mirco Calosi (2010), doi:10.1016/j.jprocont.2010.01.004


References:
[1] CHOU, C.T. VERHAEGEN, M. (1997). Subspace Algorithms for the Identification of Multivariable Dynamic Errors-in-Variables Models, Automatica, 33(10), pp. 1857-1997 doi:10.1016/S0005-1098(97)00092-7
[2] DI RUSCIO, D. (1996). DSR Toolbox, Available on request.
[3] DI RUSCIO, D. (1997). A method for identification of combined and stochastic systems, In: Applications of computer Aided Time Series Modeling, Lecture Notes in Statistics 119, Eds. M. Aoki and A. M. Havenner, Springer Verlag, ISBN 0-387-94751-5.
[4] DI RUSCIO, D. (2003). Subspace System Identification of the Kalman Filter, Modeling, Identification and Control, Vol. 24, No. 3, pp. 125-157 doi:10.4173/mic.2003.3.1
[5] GUSTAFSSON, T. (2001). Subspace identification using instrumental variable techniques, Automatica, 37, pp. 2005-2010 doi:10.1016/S0005-1098(01)00153-4
[6] JANSSON, M. (2003). Subspace Identification and ARX Modelling, In 13´th IFAC Symphosium on System Identification Rotterdam, The Netherlands, Aug 27-29.
[7] JAZWINSKI, A. FL (1970). Stochastic Process And Filtering Theory, Academic Press.
[8] SÖDERSTRÖM, T. STOICA, P. (1983). Instrumental Variable methods for System Identification, Springer-Verlag doi:10.1007/BFb0009019
[9] VAN OVERSCHEE, P. DE MOOR, B. (1996). Closed loop subspace system identification, Internal report. Katholieke Universiteit Leuven. Departement Elektrotechniek. ESAT-SISTA/TR 1996-521.
[10] VAN OVERSCHEE, P. DE MOOR, B (1997). Closed loop subspace system identification, In the proceedings of the 36th Conference on Decision and Control 1997, San Diego, California, December 6-14.


BibTeX:
@article{MIC-2005-3-3,
  title={{Closed Loop Subspace Identification}},
  author={Nilsen, Geir W. and Di Ruscio, David},
  journal={Modeling, Identification and Control},
  volume={26},
  number={3},
  pages={151--164},
  year={2005},
  doi={10.4173/mic.2005.3.3},
  publisher={Norwegian Society of Automatic Control}
};

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