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Application of digital filtering techniques

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Part of the book series: International Series on Microprocessor-Based Systems Engineering ((ISCA,volume 7))

Abstract

Continuous-time system identification usually consists of two main parts: signal processing (or pre-filtering) and parameter estimation. Both analog and digital pre-filters for signal processing can be used, where analog pre-filters are implemented in a digital computer by using such techniques as the numerical integration and the bilinear transformation. As for parameter estimation, an emphasis is put on on-line identification algorithms. Using the pre-filters of digital form, a discrete-time identification model which retains the continuous-time model parameters is derived. Some fundamental identification methods such as the least squares method, bias-compensating methods and instrumental variable methods are reviewed. Finally the choice of the input signal is discussed with simulation experiments.

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References

  • Äström, K.J. and Bohlin, T. (1965), “Numerical identification of linear dynamic systems from normal operating records”, IFAC Symp. on Theory of Adaptive Control Systems, Plenum Press, pp.96–111.

    Google Scholar 

  • Eykhoff, P. (1974), “System Identification”, Wiley, New York.

    Google Scholar 

  • Kaya, Y. and Yamamura, S. (1962), “A self adaptive system with a PID controller”, AIEE Trans. App. Ind., vol.80, 378.

    Google Scholar 

  • Lancaster, P. and Tismenetsky, M. (1985), “The Theory of Matrices — Second Edition with Applications”, Academic Press, Orlando.

    Google Scholar 

  • Ljung, L. (1971), “Characterization of the concept of ‘persistently exciting’ in the frequency domain”, Report 7119, Division of Automatic Control, Lund Institute of Technology, Sweden.

    Google Scholar 

  • Ljung, L. and Söderström, T. (1983), “Theory and Practice of Recursive Identification”, The MIT Press, Cambridge, Mass.

    MATH  Google Scholar 

  • Mitani, M. (1988), “Design of Digital Filters”, Shoukoudou, Tokyo (in Japanese).

    Google Scholar 

  • Neumann, D., Isermann, R. and Nold, S. (1988), “Comparison of some parameter estimation methods for continuous-time models”, Preprints of 8th IFAC/IFORS Symp. on Identification and System Parameter Estimation, (Beijing, August 1988), pp.1171–1176.

    Google Scholar 

  • Oppenheim, A.V. and Schafer, R.W. (1975), “Digital Signal Processing”, Prentice-Hall, New Jersey.

    MATH  Google Scholar 

  • Rao, G.P. (1983), “Piecewise Constant Orthogonal Functions and Their Applications to Systems and Control”, Springer, Berlin.

    Book  Google Scholar 

  • Sagara, S. and Zhao, Z.Y. (1987), “Parameter identification in continuous systems via numerical integration”, Technology Reports of the Kyushu University, vol.60, pp.443–449 (in Japanese).

    Google Scholar 

  • Sagara, S. and Zhao, Z.Y. (1988), “On-line identification of continuous systems using the operation of numerical integration”, Trans. IEE of Japan, vol. l08-C, pp.603–610 (in Japanese).

    Google Scholar 

  • Sagara, S. and Zhao, Z.Y. (1989), “Recursive identification of transfer function matrix in continuous systems via linear integral filter”, Int. J. Control, vol.50, pp.457–477.

    Article  MathSciNet  MATH  Google Scholar 

  • Sagara, S. and Zhao, Z.Y. (1990), “Numerical integration approach to on-line identification of continuous time systems”, Automatica, vol.26, pp.63–74.

    Article  MathSciNet  MATH  Google Scholar 

  • Sinha, N.K. (1972), “Estimation of transfer function of continuous systems from sampled data”, IEE Proc, vol.119, pp.612–614.

    Google Scholar 

  • Söderström, T. and Stoica, P. (1989), “System Identification”, Prentice Hall, London.

    MATH  Google Scholar 

  • Unbehauen, H. and Rao, G.P. (1987), “Identification of Continuous Systems”, North-Holland, Amsterdam.

    MATH  Google Scholar 

  • Unbehauen, H. and Rao, G.P. (1990), “Continuous-time approaches to system identification-a survey”, Automatica, vol.26, pp.23–35.

    Article  MathSciNet  MATH  Google Scholar 

  • Whitfield, A.H. and Messali, N. (1987), “Integral-equation approach to system identification”, Int. J. Control, vol.45, pp.1431–1445.

    Article  Google Scholar 

  • Young, P.C. (1965), “Process parameter estimation and adaptive control”, In P.H. Hammond (Ed.), “Theory of Self Adaptive Control Systems”, Plemum Press, New York.

    Google Scholar 

  • Young, P.C. (1970), “An instrumental variable method for real-time identification of a noisy process”, Automatica, vol.6, pp.271–287.

    Article  Google Scholar 

  • Young, P.C. (1981), “Parameter estimation for continuous-time models — a survey”, Automatica, vol.17, pp.23–39.

    Article  MATH  Google Scholar 

  • Young, P.C. and Jakeman, A. (1980), “Refined instrumental variable methods of recursive time-series analysis. Part III. Extensions”, Int. J. Control, vol.31, pp.741–764.

    Article  MATH  Google Scholar 

  • Zhao, Z.Y., Sagara, S. and Wada, K. (1990), “Bias-compensating least squares method for identification of continuous-time systems from sampled data”, Int. J. Control (to appear).

    Google Scholar 

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© 1991 Springer Science+Business Media Dordrecht

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Sagara, S., Zhao, Z.Y. (1991). Application of digital filtering techniques. In: Sinha, N.K., Rao, G.P. (eds) Identification of Continuous-Time Systems. International Series on Microprocessor-Based Systems Engineering, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3558-0_10

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  • DOI: https://doi.org/10.1007/978-94-011-3558-0_10

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-5576-5

  • Online ISBN: 978-94-011-3558-0

  • eBook Packages: Springer Book Archive

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