Abstract
Several approaches to estimating the parameters of a continuous-time model of a linear multivariable system from an equivalent discrete-time model are presented. It is assumed that a suitable discrete-time model has been obtained from the samples of input and output observations using techniques that are already well established. Here, our emphasis is on transformations which will lead to a suitable continuous-time model from the identified discrete-time model. Several algorithms for such transformation are critically compared. Finally, a straightforward procedure for determining a continuous-time model from the δ-model is described.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bingulac, S., and Sinha, N.K. (1989), “On the identification of continuous time multivariable systems from samples of input-output data”, Proc. Seventh Int.Conf. on Mathematical and Computer Modelling, (Chicago, 111, August 1989), pp. 231–239.
Bingulac, S. and Cooper, D.L. (1990), “Derivation of discrete and continuous time ramp invariant representations”, Electronics Letters, vol. 26, pp. 664–666.
Cooper, D. and Bingulac, S. (1990), “Computational improvement in the calculation of the natural log of a square matrix”, Electronics Letters, vol. 26, pp. 861–862.
El-Sherief, H., and Sinha, N.K. (1979), “Choice of models for the identification of linear multivariable discrete-time systems”, Proc. I.E.E., vol. 126, pp. 1326–1330.
Feliu, V. (1986), “A transformation algorithm for estimating system Laplace transform from sampled-data”, IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-16, pp. 168–173.
Feliu, V., Cerrada, J.A., and Cerrada, C. (1988), “An algorithm to compute the continuous state model from its equivalent discrete model”, Control-Theory and Advanced Technology, vol. 4, pp. 231–241.
Guidorzi, R. (1975), “Canonical structures in the identification of multivariable systems”, Automatica, vol. 11, pp. 113–116.
Haykin, S.S. (1972), “A unified treatment of recursive digital filtering”, IEEE Transactions on Automatic Control, vol. AC-17, pp. 113–116.
Hildebrand, F.B. (1956), “Introduction to Numerical Analysis”, McGraw Hill, New York, 1983.
Hsia, T.C. (1972), “On sampled-data approach to parameter identification of continuous linear systems”, IEEE Transactions on Automatic Control, vol. AC-17. pp. 247–249.
Lastman, G.J., Puthenpura, S, and Sinha, N.K. (1984), “Algorithm for the identification of continuous-time multivariable systems from their discrete-time models”, Electronics Letters, vol. 20, pp. 918–919.
Middleton, G.H. and Goodwin, G.C. (1986), “Improved finite word length characteristics digital control using delta operators”, IEEE Transactions on Automatic Control, vol. AC-31, pp. 1015–1021.
Middleton, G.H. and Goodwin, G.C. (1990), “Digital Estimation and Control: A Unified Approach”, Prentice-Hall, New Jersey.
Niness, B.M. and Goodwin, G.C. (1991), “The relationship between discrete time and continuous time linear estimation”, Chapter 3 of this book.
Puthenpura, S., and Sinha, N.K. (1984), “Transformation of continuous-time model of a linear multivariable system from its discrete-time model”, Electronics Letters, vol. 20, pp. 737–738.
Puthenpura, S., and Sinha, N.K. (1985), “A procedure for determining the optimum sampling interval for system identification using a digital computer”, Can. Elec. Eng. J., vol. 10, pp. 152–157.
Puthenpura, S., and Sinha, N.K. (1987), “Extended power method and stability of linear discrete time systems”, Electronics Letters, vol.23, pp. 4–5.
Raol, J.R., Puthenpura, S.C., and Sinha, N.K. (1987), “Algorithms for transformation of multivariable discrete-time models to continuous-time models”, Advances in Modelling and Simulation, (AMSE Press), Vol. 6, pp. 52–62.
Sinha, N.K. (1972), “Estimation of transfer function of continuous-time systems from sampled data”, Proceedings IEE, vol. 126, pp. 612–614.
Sinha, N.K., and Kuszta, B. (1983), “Modeling and Identification of Dynamic Systems”, Von-Nostrand Reinhold, New York.
Sinha, N.K., Kwong, Y.H., (1979), “Recursive identification of the parameters of linear multivariable systems”, Automatica, vol. 15, pp. 471–475.
Sinha, N.K., and Lastman, G.J. (1981), “Transformation algorithm for identification of continuous-time multivariable systems from discrete data”, Electronics Letters, vol. 21, pp. 779–780.
Sinha, N.K., and Lastman, G.J. (1982), “Identification of continuous-time multivariable systems from sampled data”, International Journal of Control, vol. 35, pp. 117–126.
Sinha, N.K., and Puthenpura, S. (1985), “Choice of the optimum sampling interval for the identification of continuous-time systems from samples of input/output data”. IEE Proceedings, vol. 132, Pt. D., pp. 263–267.
Strmčnik, S., and Bremšak, F, (1979), “Some new transformation algorithms in the identification of continuous-time multivariable systems using discrete identification methods”, Preprints 5th IFAC Symposium on Identification and System Parameter Estimation (Darmstadt, West Germany), pp. 397–405.
Tse, E., and Weinert, H. (1975), “Structure determination and parameter identification for multivariable stochastic linear systems”, IEEE Transactions on Automatic Control, vol. AC-20, pp. 603–613.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Sinha, N.K., Lastman, G.J. (1991). Transformation of discrete-time models. 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_4
Download citation
DOI: https://doi.org/10.1007/978-94-011-3558-0_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-5576-5
Online ISBN: 978-94-011-3558-0
eBook Packages: Springer Book Archive