Advertisement

Linear System Identification

  • Gennady G. Kulikov
  • Haydn A. Thompson
Part of the Advances in Industrial Control book series (AIC)

Abstract

In general there are two ways of arriving at models of physical processes:
  • Physical principles modelling. Physical knowledge of the process, in the form of first principles, is employed to arrive at a model that will generally consist of a multitude of differential / partial differential / algebraic relations between physical quantities. The construction of a model is based on presumed knowledge about the physics that governs the process. The first principles relations concern, e.g., the laws of conservation of energy and mass and Newton’s law of movement.

  • Experimental modelling, or system identification. Measurements of several variables of the process are taken and a model is constructed by identifying a model that matches the measured data as well as possible.

Keywords

Cost Function Binary Signal Auto Regressive Maximum Likelihood Estimator Crest Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ljung L. System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice Hall; 1987.zbMATHGoogle Scholar
  2. 2.
    Godfrey KR. Correlation methods. Automatica, 1980;16:527–534.CrossRefzbMATHGoogle Scholar
  3. 3.
    Ljung L. System Identification Toolbox for Use with Matlab. Natick, MA: Mathworks, Inc., 1995.Google Scholar
  4. 4.
    Norton JP. An Introduction to Identification. London: Academic Press, 1986.zbMATHGoogle Scholar
  5. 5.
    Brillinger D. Time Series: Data Analysis and Theory. San Francisco: Holden-Day, 1981.zbMATHGoogle Scholar
  6. 6.
    Schoukens J, Renneboog J. Modeling the noise influence on the Fourier coefficients after a discrete Fourier transform. IEEE Trans. Instrumentation and Measurement, 1986;35:278–286.CrossRefGoogle Scholar
  7. 7.
    Gade S, Herlufsen H. Use of weighting functions in DFT/FFT analysis. Parts I and II. Briiel and Kjaer Technical Review 3 and 4, 1987.Google Scholar
  8. 8.
    Bendat JS, Piersol AG. Engineering Applications of Correlation and Spectral Analysis. New York: Wiley-Interscience, 1980.zbMATHGoogle Scholar
  9. 9.
    Guillaume P. Identification of multiinput multioutput systems using frequency-domain methods. Ph.D. dissertation. Vrije Universiteit Brüssel, Department ELEC, Belgium, 1992.Google Scholar
  10. 10.
    Schoukens J, Guillaume P, Pintelon R. Design of broadband excitation signals. (Chapter 3). In: Godfrey K, editor. Perturbation Signals for System Identi-fication. Englewood Cliffs, NJ: Prentice-Hall, 1993.Google Scholar
  11. 11.
    Pintelon R, Guillaume P, Rolain Y, Verbeyst F. Identification of linear systems captured in a feedback loop. IEEE Trans. Instrumentation and Measurement, 1992;41:747–754.CrossRefGoogle Scholar
  12. 12.
    Schoukens J, Pintelon R, Renneboog J. A maximum likelihood estimator for linear and nonlinear systems — a practical application of estimation techniques in measurement problems. IEEE Trans. Instrumentation and Measurement, 1988;37:10–17.CrossRefGoogle Scholar
  13. 13.
    Kollâr I. Frequency-Domain System Identification Toolbox for Use with Matlab. Natick, MA: Mathworks, Inc., 1994.Google Scholar
  14. 14.
    Kollâr I. On frequency-domain identification of linear systems. IEEE Trans. Instrumentation and Measurement, 1993; 42:2–6.CrossRefGoogle Scholar
  15. 15.
    Pintelon R, Schoukens J. System Identification: A Frequency-Domain Approach. IEEE Press, 2001.CrossRefGoogle Scholar
  16. 16.
    Evans C. Identification of linear and nonlinear systems using multisine signals, with a gas turbine application. Ph.D. dissertation. University of Glamorgan, School of Electronics, UK, 1998.Google Scholar
  17. 17.
    Schoukens J, Dobrowiecki T, Pintelon R. Parametric and nonparametric identification of nonlinear systems in the presence of nonlinear distortions — A frequency-domain approach. IEEE Instrumentation and Measurement Technology Conference, IMTC/98, St. Paul, USA, 1998;43:176–190.MathSciNetzbMATHGoogle Scholar
  18. 18.
    Schoukens J, Pintelon R, Van Hamme H. Identification of linear dynamic systems using piecewise constant excitations: Use, misuse and alternatives. Automatica, 1994; 30:1153–1169.CrossRefzbMATHGoogle Scholar
  19. 19.
    Hill DC. Identification of gas turbine dynamics: time-domain estimation problems. ASME Gas Turbine Conference, 97-GT-31, 1997:1–7.Google Scholar
  20. 20.
    Godfrey KR. Perturbation Signals for System Identification. Englewood Cliffs, NJ: Prentice-Hall, 1993.zbMATHGoogle Scholar
  21. 21.
    Van den Bos A. Estimation of parameters of linear system using periodic test signals. Ph.D. dissertation. Technische Hogeschool Delft, Netherlands, 1970.Google Scholar
  22. 22.
    Schroeder MR. Synthesis of low peak-factor signals and binary sequences of low auto-correlation. IEEE Trans. Information Theory, 1970; 16:85–89.CrossRefGoogle Scholar
  23. 23.
    Guillaume P, Schoukens J, Pintelon R, Kollâr I. Crest factor minimisation using nonlinear Chebyshev approximation methods. IEEE Trans. Instrumen-tation and Measurement, 1991;40:982–989.CrossRefGoogle Scholar
  24. 24.
    Kollâr I, Pintelon R, Schoukens J. Frequency-domain system identification toolbox for Matlab: A complex application example. Prepr. 10th IFAC Symp. on System Identification, Denmark, 1994;4:23–28.Google Scholar
  25. 25.
    Schoukens J, Pintelon R, Vandersteen G, Guillaume P. Frequency-domain system identification using nonparametric noise models estimated from a small number of data sets. Automatica, 1997;33:1073–1086.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Evans C, Rees D, Jones L. Identifying linear models of systems suffering nonlinear distortions, with a gas turbine application. IEE Proc. Control Theory and Applications, 1995 ; 142:229–240.Google Scholar
  27. 27.
    McCormack AS, Godfrey KR, Flower JO. The detection of and compensation for nonlinear effects using periodic input signals. IEE International Conference “Control 94” University of Warwick, 1994:297–302.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Gennady G. Kulikov
    • 1
  • Haydn A. Thompson
    • 2
  1. 1.Department of Automated Control SystemsUfa State Aviation Technical UniversityRussia
  2. 2.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

Personalised recommendations