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Continuous-time dynamic system identification with multisine random excitation revisited

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a new, revisited and unified approach to a linear continuous-time dynamic single-input single-output system identification using input and output signal samples acquired with a deterministic constant or random sampling interval. The approach is based on a specially designed identification experiment with excitation of the form of a continuous-time multisine random excitation and digital processing of the corresponding signal samples obtained without analogue antialiasing filtration in the case of disturbances satisfying or not satisfying the Shannon's sampling theorem. Properties of the proposed approach are discussed taking into account nonlinearity of the excitation generation and data acquisition systems with a focus on model identification in the case of input and output signal levels comparable with data acquisition system accuracy. Methods reducing influence of the disturbances (including aliasing) as well as nonlinearities of the excitation generation and data acquisition systems on identification results are proposed, too.
Rocznik
Strony
133--149
Opis fizyczny
Bibliogr. 64 poz., wzory
Twórcy
autor
  • Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • [1] J. S. BENDAT and A. G. PIERSOL: Random data analysis and measurement procedures. John Wiley & Sons, Inc., New York, 1986.
  • [2] J. S. BENDAT and A. G. PIERSOL: Engineering applications of correlation and spectral analysis. John Wiley & Sons, Inc., New York, 1993.
  • [3] I. BILINSKIS and I. MEDNIEKS: Introduction to digital alias-free signal processing. Institute of Electronics and Computer Science, Riga, 2001.
  • [4] I. BILINSKS: Digital alias-free signal processing. John Willey & Sons, Ltd., Chichester, 2007.
  • [5] R. E. BLAHUT: Fast algorithms for digital signal processing. Addison-Wesley Publishing Co., 1985.
  • [6] C. T. CHOU, M. VERHAEGEN and R. JOHANSSON: Continuous-time identification of SISO systems using Laguerre functions. IEEE Trans. on Signal Processing, 47, (1999), 349-362.
  • [7] R. CHRISTENSEN: Linear models for multivariate, time-series, and spatial data. Springer-Verlag, 1991.
  • [8] K. CZY˙Z: Signal reconstruction with oversampling for active noise control. Proc. of the 12th IEEE Int. Conf. on Methods and Models in Automation and Robotics, Mi˛edzyzdroje, Poland, 1, (2006), 1033-1036.
  • [9] K. CZY˙Z: Active noise control systems with nonuniform signal sampling. Jacek Skalmierski Computer Studio, Gliwice, 2007.
  • [10] A. M. FAROK: Nonuniform sampling: Theory and practice. Kluver Academic Publishers, New York, 2001.
  • [11] J. FIGWER: Identification of multi-input spactral models: Ph.D. Thesis, Technical report, The Silesian University of Technology, Institute of Automatic Control, Gliwice, 1992, (in Polish).
  • [12] J. FIGWER: Multisine excitation for process identification. Archives of Control Sciences, 5, (1996), 279–295.
  • [13] J. FIGWER: Random noise synthesis and generation. Proc. of the 3rd Int. Symp. on Methods and Models in Automation and Robotics, Mi˛edzyzdroje, Poland, 1, (1996), 83-88.
  • [14] J. FIGWER: A new method of random time-series simulation. Simulation Practice and Theory, 5, (1997), 217-234.
  • [15] J. FIGWER: Continuous-time system identification with multisine excitation. Proc. of the 4th Int. Symp. on Methods and Models in Automation and Robotics, Mi˛edzyzdroje, Poland, 1, (1997), 317-322.
  • [16] J. FIGWER: Two-input system identification using bivariate multisine excitation. Proc. of the 5th Int. Symp. on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, (1998), 571-576.
  • [17] J. FIGWER: Random process synthesis and simulation. Zeszyty Naukowe Politechniki S´ la˛skiej, Seria Automatyka, Gliwice, 126 (1999).
  • [18] J. FIGWER: Closed-loop system identification with multisine excitation. Proc. of the 8th IEEE Int. Conf. on Methods and Models in Automation and Robotics, Szczecin, Poland, (2002), 477-482.
  • [19] J. FIGWER: Identification of secondary path in active noise control. XIV Krajowa Konferencja Automatyki, Zielona Góra, Poland, (2002), (in Polish).
  • [20] J. FIGWER: Model identification and update under operation of active noise control systems. Pomiary Automatyka Kontrola, 11 (2003), 22-25.
  • [21] J. FIGWER: Multisine transformation – properties and applications. Nonlinear Dynamics, 35 (2004), 331-346.
  • [22] J. FIGWER: Identification ofWiener models with multisine random excitation. XV Krajowa Konferencja Automatyki,Warsaw, Poland, 1, (2005), 343-348, (in Polish).
  • [23] J. FIGWER: Multisine random rumber generator. Jacek Skalmierski Computer Studio, Gliwice, 2007.
  • [24] J. FIGWER: Nonlinear secondary path model identification. Proc. of the 14th Int. Congress on Sound and Vibration, Cairns, Australia, (2007), 71-78.
  • [25] J. FIGWER: Adaptive synthesis and generation of random fields. Jacek Skalmierski Computer Studio, Gliwice, 2008.
  • [26] J. FIGWER: Nonlinear systems modeling. In: In˙zynieria Wiedzy i Systemy Ekspertowe, Ed. A. Grzech, K. Juszczyszyn, H. Kwa´snicka, N.T. Nguyen, Akademicka Oficyna Wydawnicza EXIT, Warsaw, (2009), 89-100,
  • [27] J. FIGWER and K. CZY˙Z: Identification of continuous dynamical systems with multirate sampled signals. XV Krajowa Konferencja Automatyki, Warsaw, Poland, 2 (2005), 317-322, (in Polish).
  • [28] J. FIGWER and A. NIEDERLI´N SKI: Multisine series for simulation and identification. Technical Report, The Silesian University of Technology, Institute of Automatic Control, Gliwice, 1992.
  • [29] J. FIGWER, A. NIEDERLI´N SKI and J. KASPRZYK: A new approach to the identification of linear discrete-time MISO systems. Proc. of the 9th IFAC/IFORS Symp. on Identification and System Parameter Estimation,, Budapest, Hungary, (1991), 1220-1225.
  • [30] J. FIGWER, A. NIEDERLI´N SKI and J. KASPRZYK: A new approach to the identification of linear discrete-time MISO systems. Archives of Control Sciences, 2, (1993), 223-239.
  • [31] H. GARNIER, M. GILSON and E. HUSELSTEIN: Developments for the Matlab CONTSID Toolbox. Proc. of the 13th IFAC Symp. on System Identification, Rotherdam, (2003), 1007-1012.
  • [32] H. GARNIER, M. MENSLER and A. RICHARD: Continuous-time model identification from sampled data: implementation issues and performance evaluation. Int. J. of Control, 76, (2003), 1337-1357.
  • [33] H. GARNIER and L. WANG: Identification of continuous-time models from sampled data. Springer-Verlag, London, 2008.
  • [34] J. GILBBERG and L. LJUNG: Frequency domain identification of continuoustime output error models, Part I: Uniformly sampled data and frequency function approximation. Automatica, 46 (2010), 1-10.
  • [35] J. GILBBERG and L. LJUNG: Frequency domain identification of continuoustime output error models, Part II: Non-uniformly sampled data and B-spline output approximation. Automatica, 46 (2010), 11-18.
  • [36] T. GŁÓWKA and J. FIGWER: A new approach to frequency response identification. Proc. of the 13th IEEE Int. Conf. on Methods and Models in Automation and Robotics, Szczecin, Poland, (2007), 555-558.
  • [37] C. H. HANSEN and S.D. SNYDER: Active control of noise and vibration. Cambridge University Press, 1997.
  • [38] F. HARRIS: On the use of windows for harmonic analysis with discrete Fourier transform. Proc. of the IEEE, 66 (1978), 51-83.
  • [39] K. J. HARRISON, J.R. PARTINGTON and J.A. WARD: Input-output identifability of continuous-time linear systems. J. of Complexity, 18 (2001), 210-223.
  • [40] G. M. JENKINS and D.G. WATTS: Spectral analysis and its applications. Holden-Day, San Francisco, 1968.
  • [41] R. JOHANSSON: System modeling and identification. Prentice Hall, Englewood Cliffs, New Jersey, 1993.
  • [42] R. JOHANSSON: Identification of continuous-time models. IEEE Trans. on Signal Processing, 42 (1994), 887-897.
  • [43] R. JOHANSSON, M. VERHAEGEN and C.T. CHOU: Stochastic theory of continuous-time statespace identification. IEEE Trans. on Signal Processing, 47 (1999), 41-51.
  • [44] S. M. KAY: Modern spectral estimation: Theory and applications. Prentice Hall, Englewood Cliffs, 1986.
  • [45] S. M. KUO and D.R. MORGAN: Active noise control systems. Algorithms and DSP implementations. J. Wiley & Sons, Inc., New York, 1996.
  • [46] L. LJUNG: System identification - Theory for the user. Prentice Hall, New Jersey, 1999.
  • [47] K. MAHATA and H. GARNIER: Identification of continuous-time errors-invariables models. Automatica, 42 (2006), 1477-1490.
  • [48] S. L. MARPLE: Digital spectral analysis with applications. Prentice Hall, Englewood Cliffs, 1987.
  • [49] S. K. MITRA and F.J. KAISER: Handbook for dgital signal processing. JohnWiley & Sons, New York, 1993.
  • [50] A. NIEDERLI´NSKI, J. KASPRZYK and J. FIGWER: Expert system for process identification. User’s manual. Centrum Naukowo-Produkcyjne Systemów Sterowania MERASTER, Katowice, 1990.
  • [51] A. NIEDERLI´NSKI, J. KASPRZYK and J. FIGWER: MULTI-EDIP analyzer of multidimensional signals and systems. Wydawnictwo Politechniki S´ la˛skiej, Gliwice, 1997, (in Polish).
  • [52] P. A. NELSON and S.J. ELIOT: Active control of sound. Academic Press, London, 1992.
  • [53] U. S. PILLAI and JT.I. SHIM: Spectrum estimation and system identification. Springer-Verlag, New York, 1993.
  • [54] R. PINTELON and J. SCHOUKENS: Identification of continuous-time systems using arbitrary signals. Automatica, 33, (1997), 991-994.
  • [55] R. PINTELON and J. SCHOUKENS: System identification. A frequency domain approach. IEEE Press, New York, 2001.
  • [56] G. P. RAO and H. UNBEHAUEN: Identification of continuous-time systems. IEE Proceedings on Control Theory Applications, 153, (2006), 185-220.
  • [57] D. C. SAHA and G. P. RAO: Identification of continuous dynamical systems – the Poisson moment functional (PMF) approach. Springer-Verlag, Berlin, 1983.
  • [58] N. K. SINHA and G. P. RAO: Identification of continuous-time systems: Methodology and computer implementations. Kluwer Academic Publishers, Dordrecht, 1991.
  • [59] T. SÖDERSTRÖM and P. STOICA: System identification. Prentice Hall International, London, 1989.
  • [60] P. STOICA and R.L. MOSES: Introduction to spectral analysis. Prentice Hall, 1987.
  • [61] J. K. TUGNAIT and Y. ZHOU: On closed-loop system identification using polyspectral analysis given noisy input-output time-domain data. Automatica, 36 (2000), 1795-1808.
  • [62] H. UNBEHAUEN and G.P. RAO: Identification of continuous-time systems. North Holland Systems and Control Series, Amsterdam, 1987.
  • [63] P. C. YOUNG: Parameter estimation for continuous-time models – A survey. Automatica, 17, (1981), 23-29.
  • [64] J. ZARZYCKI: Orthogonal digital filtering of stochastic signals. Wydawnictwa Naukowo-Techniczne, Warsaw, Poland, 1998, (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0073-0002
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