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Authors
Jorn Op Den Buijs; Lizette Warner; Nicolas W. Chbat; Hal H. Ottesen

Identifier
10.3182/20060329-3-AU-2901.00053

Index Terms
order,piecewise,recursive,system identification

Abstract
In this paper, we propose a novel method for the real-time identification and simultaneous order estimation of a class of piecewise linear systems, namely jump-linear systems. The parameters, orders and time instances of switching are identified, based on a recursive weighted least-squares scheme and pole-zero cancellations. Several examples demonstrate the effectiveness of the algorithm. The proposed real-time technique is promising for use in patient monitoring and feedback control systems.

References

[1] Bemporad, A., A. Garulli, S. Paoletti and A. Vicino
     (2003). A greedy approach to identification
     of piecewise a fine models. Proc. of the
    6th Int. Workshop on Hybrid Systems: Computation
     and Control pp. 97-112.
[2] Bliley, K., D. Holmes III, P. Kane, R. Foster,
    J. Levine, E. Daniel and B. Gilbert (2005).
    A miniaturized low power personal motion
    analysis logger utilizing mems accelerometer
    and low power microcontroller. IEEE-EMBS
    Special Topic Conf. on Microtechnologies in
    Medicine and Biology pp. 92-93.
[3] Eure, K. (2002). Implementation of real-time
    system identification using recursive techniques.
     Proc. of the American Control Conf.
    pp. 4807-4812.
[4] Ferrari-Trecate, G., M. Muselli, D. Liberati and
    M. Morari (2003). A clustering technique
    for the identification of piecewise affine and
    hybrid systems. Automatica 39, 205-217.
[5] Juloski, A., S. Weiland and W. Heemels (2004a).
    A bayesian approach to identification of hybrid
     systems. Proc. of the 43rd IEEE CDC
    pp. 13-19.
[6] Juloski, A., W. Heemels and G. Ferrari-Trecate
    (2004b). Data-based hybrid modeling of
    the component placement process in pick-and-place
     machines. Control Eng. Practice
    12, 1241-1252.
[7] Kezler, M. (2005). Volume-targeted ventilation. J.
    Perinatol. 25, 19-22.
[8] Maran, A., C. Crepaldi, A. Avogaro, S. Catuogno,
    A. Burlina, A. Poscia and A. Tiengo (2004).
    Continuous glucose monitoring in conditions
    other than diabetes. Diabetes Metab. Res.
    Rev. 20, 50-55.
[9] Muller, S., A. Kloppe, A. Mugge and J. Werner
    (2004). A fiberoptic sensor system for cardiac
    monitoring and electrotherapy. Biomedizinische
     Technik 49, 311-315.
[10] Niessen, J., A. Juloski, G. Ferrari-Trecate and
     W. Heemels (2004). Comparison of three procedures
      for the identification of hybrid systems.
      Proc. of the IEEE Int. Conf. Control
     Applications pp. 643-648.
[11] Oku, H., G. Nijsse, M. Verhaegen and V. Verdult
     (2001). Change detection in the dynamics
     with recursive subspace identification. Proc.
     of the 40th IEEE CDC pp. 2297-2302.
[12] Op Den Buijs, J., A. Juloski, T. Ivanics, L. Ligeti,
     G. Van Der Vusse and N. Van Riel (2004).
     Identification of a switching model of calcium
     cycling in isolated rat hearts. Proc. of the 26th
     Conf. IEEE EMBS pp. 841-844.
[13] Petridis, V. and A. Kehagias (1998). Identification
     of switching dynamical systems using multiple
      models. Proc. of the 37th IEEE CDC
     pp. 199-204.
[14] Sieg, A., R. Guy and M. Delgado-Charro (2005).
     Noninvasive and minimally invasive methods
     for transdermal glucose monitoring. Diabetes
     Technology and Therapeutics 7, 174-197.
[15] Sinha, N. and B. Kusta (1983). Modeling and
     identification of dynamic systems. Van Nostrand
      Reinhold. New York.
[16] Skeppstedt, A., L. Ljung and M. Millnert (1992).
     Constuction of composite models from observed
      data. Int. J. Control 55, 141-152.
[17] Vidal, R., S. Soatto and S. Sastry (2003). An algebraic
      geometric approach to the identification
     of linear hybrid systems. Proc. of the 42nd
     IEEE CDC.
[18] Xia, L. and J. Moore (1989). Recursive identification
      of overparametrized systems. IEEE
     Trans. Automatic Control 34, 327-331.