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Introduction to System Identification Using Parameter Estimation Methods

  • H. Unbehauen
Part of the International Centre for Mechanical Sciences book series (CISM, volume 272)

Abstract

The rapid progress in the field of computer technology during recent years has led to an increasing use of process computers to analyse, supervise and control technical processes. Process computers provide the opportunity to sample and store measuring data in a short time, and to process them immediately for better process operation (e.g. with optimum efficiency, best product quality, optimum time behaviour etc.). An important condition for optimum process operation is the knowledge of the process behaviour in the past, at present and in the future. The use of computers allows a permanent analysation or identification of the process dynamics by evaluating the input and output signals of the system. The result of such a process identification is usually a mathematical model, by which the static and dynamic behaviour can be estimated or predicted.

Keywords

Output Signal Instrumental Variable Parameter Estimation Method Structural Coefficient Auxiliary Model 
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.

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References

  1. Akaike, H. (1969), Fitting autoregressive models for prediction. Ann. Inst. Statist. Math., 21, pp. 243–247.CrossRefMATHMathSciNetGoogle Scholar
  2. Akaike, H. (1970), Statistical predictor identification. Ann. Inst. Statist. Math., 22, pp. 203–217.CrossRefMATHMathSciNetGoogle Scholar
  3. Akaike, H. (1978), On newer statistical approaches to param. est. and structure determination. IFAC WK Helsinki, pp. 1877–1884.Google Scholar
  4. Aström, K.J. (1968), Lectures on the ident. problem - the L.S. method. Lund Inst. of Techn., Rep. 6806.Google Scholar
  5. Aström, K.J. (1970), Introduction to stochastic control theory. Academic Press, N.Y.MATHGoogle Scholar
  6. Aström, K.J., Källström. C. (1972), Ident. and modelling of ship dynamics. Lund Inst. of Techn., Rep. 7202.Google Scholar
  7. Bauer, B. (1977), Parameterschätzverfahren zur on-line Ident. Dynamischer Systeme im offenen und geschlossenen Regelkreis. Diss. Ruhr-Universität Bochum.Google Scholar
  8. Baur, U. (1976), On-line Parameterschätzverfahren zur Identifikation linearer dynamischer Prozesse mit ProzeBrechnern - Entwicklung, Vergleich, Erprobung. Diss. Universität Stuttgart und PDV-Bericht KFKPDV 65, Gesellschaft für Kernforschung, Karlsruhe.Google Scholar
  9. Bingulac, S.P., Bassani (1976), CAD of linear systems using L-A-S language, Colloquio Franco-Brasileiro, Rio de Janeiro.Google Scholar
  10. Bohlin, T. (1968), The ML method of ident., IBM Schweden, paper 18. 191.Google Scholar
  11. Boom, v.d., Emden, v.d. (1974), The determination of the orders of process and noise dynamics, Automatica, V.10, pp. 245–256.Google Scholar
  12. Boom, v.d., A.J.W., Lemmens, W.J.M. (1977), Sater, an interactive program package, IFAC Symp. Trends in Aut. Control Education, Barcelona.Google Scholar
  13. Bosch, P.P.J., Bruyn, P.M. (1977), The dedicated digital computer, IFAC Symp. Trends in Aut. Control Education, Barcelona.Google Scholar
  14. Chow, J.C. (1972), On estimating the order of an autoregressive moving average process with uncertain observations. IEEE Tr. on AC-17, pp. 707–709.Google Scholar
  15. Clarke, D.W. (1967), Generalized LS est. of the param. of a dynamical model. IFAC Symp. Prag, pap. 3. 17.Google Scholar
  16. Edmunds, J.M. (1979), Cambridge linear analysis and design programs, IFAC Symp. ‘CAD of Control Systems’, Zürich.Google Scholar
  17. Eykhoff, P. (1967), Process param. and state est., IFAC Symp. Prag,pap.o. 2.Google Scholar
  18. Eykhoff, P. (1973), Identification and system parameter estimation. 3rd IFAC-Symposium, Hague.Google Scholar
  19. Göhring, B. (1973), Erprobung statistischer Parameterschätzmethoden and Strukturprüfverfahren zur exp. Ident. von Regelsystemen. Diss. Universität Stuttgart.Google Scholar
  20. Goodwin, G.C., Payne, R.L., Kabaila, P. (1976), On canonical forms and algorithms for multivariable time series analysis. IV. IFAC Symposium Tbilisi, USSR.Google Scholar
  21. Gustaysson, I., (1972), A comparison of different methods for ident. of industrial processes. Automatica, 8, pp. 127–142.CrossRefGoogle Scholar
  22. Hastings-James, R., Sage, M.W. (1969), Recursive GLS procedure for on- line ident. of process param., Proc. IEE, 116, pp. 2057–2062.Google Scholar
  23. Isermann, R. and others (1973), Comparison and evaluation of six on-line ident. and param. est. methods with three simulates processes. IFAC Symposium, The Hague, pap. E-1.Google Scholar
  24. Isermann, R., Dymchiz, E. (1976), A software package for process computer aided control system design, IFAC/IFIP Symp. Computer Control, Talinn, USSR.Google Scholar
  25. Isermann, R. (1979), Identification and system parameter estimation. 5th IFAC Symposium, Darmstadt.Google Scholar
  26. Källström, C.G., Essebo, T., Aström, K.J. (1976), A computer program for maximum likelihood identification of linear, multivariable stochastic systems, IFAC Symp. Tbilisi, USSR.Google Scholar
  27. Keviczky, L.K., Bânyâsz, Cs.M. (1976), Some new results on multiple input-multiple Output identification methods, IFAC Symp. Tbilisi, USSR.Google Scholar
  28. Mc Corkell, L. (1977), CAD system analysis and control for undergraduates, IFAC Symp. ‘Trends in Aut. Control Education’, Barcelona.Google Scholar
  29. Niemann, R. (1971), A review of proc. ident. and param. est. techn. Int. J. Cont., 12, pp. 209–264.CrossRefGoogle Scholar
  30. Rajbman, V. (1976), Identification and system parameter estimation. 4th IFAC-Symposium, Tbilisi, USSR.Google Scholar
  31. Richalet, J., Foigel, J.K. (1979), Self-adapting IDCOM. 5th IFAC-Symp. Darmstadt.Google Scholar
  32. Saridis, G.N. (1974), Comparison of six on-line ident. algorithms, Automatica, 10, pp. 69–79.CrossRefMATHGoogle Scholar
  33. Schmid, Chr., Unbehauen, H. (1979), KEDDC, a general purpose CAD software system for application in control engineering, IFAC Symp. SOCOCO, Prague.Google Scholar
  34. Söderström, T. (1977), On model structure testing in system ident., Int. J. Control, 26, pp. 1–18.CrossRefMATHGoogle Scholar
  35. Talmon, J.I. schemes. (1971), Approximated Gauss-Markov estimators and related Eindhoven Univ. of Techn., Rep. 71-E-17.Google Scholar
  36. Unbehauen, H. Systeme. (1973a), Übersicht über Methoden zur Ident. dynamischer Regelungstechnik, 21, pp. 2–8.Google Scholar
  37. Unbehauen, H., Gôhring, B. (1973b), Application of different statistical tests for the determination of most accurate order of the model in param. est., IFAC Symp. Hague, p. TS-1.Google Scholar
  38. Wellstead, P.E. (1978), An instrumental product moment test for model order estimation, Automatica, V.14, pp. 89–91.Google Scholar
  39. Wieslander, J. (1976), IDPAC-user’s guide, Lund Institute of Technology, Rep. 7605.Google Scholar
  40. Wong, K.Y., Polak, E. (1967), Ident. of linear discrete time systems using IV method, IEEE Tr. AC, 12, pp. 707–718.CrossRefGoogle Scholar
  41. Woodside, C.M. (1971), Estimation of the order of linear systems. Automatica, V.7, pp. 727–733.Google Scholar
  42. Young, P.C. (1970), An instrumental variable method for realtime identification of a noisy process. Automatica, 6, pp. 271–286.CrossRefGoogle Scholar
  43. Young, P., Jakemann, A., Mc Murtrie, R. (1980), An instrumental variable method for model order identification, Automatica, V. 16, pp. 281–294.CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 1982

Authors and Affiliations

  • H. Unbehauen
    • 1
  1. 1.Ruhr-UniversitätBochumGermany

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