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New Results in System Identification

  • Donald M. Wiberg

Abstract

This tutorial selectively presents significant results in system identification that have appeared in the last decade. Specifically, (1) transfer function bias, (2) system order estimation by predictive least squares, (3) near optimal recursive parameter estimation, (4) testing against a no-noise model, and (5) robustness theory for estimators are presented. No attempt is made at completeness, and the author’s taste is the criterion for selection of specific results for presentation.

Keywords

Extended Kalman Filter American Control Conf Transfer Function Estimation Convergent Approximation Prediction Error Method 
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. 1.
    L. Ljung. “System Identification Theory for the User”, Prentice Hall, Englewood Cliffs, NJ (1987).Google Scholar
  2. 2.
    L. Ljung and T. Söderström. “Theory and Practice of Recursive Identification”, MIT Press, Cambridge, MA (1983).Google Scholar
  3. 3.
    T. Söderström and P. Stoica. “System Identification”, Prentice Hall, Englewood Cliffs, NJ (1989).Google Scholar
  4. 4.
    B. Wahlberg and L. Ljung. Design variables for bias distribution in transfer function estimation, IEEE Trans. Automatic Control, AC-31, 134:144 (1986).Google Scholar
  5. 5.
    H. Akaike. A new look at statistical model identification, IEEE Trans. Automatic Control, AC-19, 716:723 (1974).Google Scholar
  6. 6.
    J. Rissanen. Order estimation by accumulated prediction errors, in: “Essays in Time Series and Allied Processes”, J. Gani and M.B. Priestley, eds., Applied Probability Trust, Sheffield, England (1986).Google Scholar
  7. 7.
    D.M. Wiberg. Another approach to on-line parameter estimation, Proc. 1987 American Control Conf), 1, 418:423 (1987)Google Scholar
  8. 8.
    D.G. DeWolf and D.M. Wiberg. An ordinary differential equation technique for continuous time parameter estimation, Proc. 1991 American Control Conf., 2, 1390:1397 (1991). Also accepted, IEEE Trans. Automatic Control.Google Scholar
  9. 9.
    D.M. Wiberg and D.G. DeWolf. A convergent approximation of the optimal parameter estimator, Proc. 30th Conf. on Decision and Control, 2, 2017:2023 (1991).Google Scholar
  10. 10.
    D.M. Wiberg and L.A. Campbell. A discrete-time convergent approximation of the optimal recursive parameter estimator, Proc. 9th IFAC/IFORS Conf. on Identification and System Parameter Estimation, 1, 140:144 (1991).Google Scholar
  11. 11.
    D.M. Wiberg. The MIMO discrete-time convergent approximation of the optimal recursive parameter estimator, Proc. 1992 American Control Conf., to appear.Google Scholar
  12. 12.
    D.S. Ward, J.I. Jensen, D.M. Wiberg, and J.W. Bellville. Noise models in respiration, Proc. 1983 American Control Conf, 1, 31:35 (1983).Google Scholar
  13. 13.
    W.S. Levine and R.T. Reichert. An introduction to H control system design, Proc. 29th Conf. on Decision and Control, 6, 2966:2974 (1990).Google Scholar
  14. 14.
    T. Basar and P. Bernahrd. “H -Optimal Control and Related Minimax Design Problems”, Birkhäuser, Boston (1991).Google Scholar
  15. 15.
    A.J. Helmicki, C.A. Jacobsen, and C.N. Nett. Control oriented system identification: a worst case/deterministic approach in H , IEEE Trans, on Automatic Control, 36, 1163:1176 (1991).Google Scholar
  16. 16.
    K.M. Eveker and C.N. Nett. Model development for active surge control/rotating stall avoidance in aircraft gas turbine engines, Proc. 1991 American Control Conf, 3, 3166:3172 (1991).Google Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Donald M. Wiberg
    • 1
  1. 1.Electrical Engineering DepartmentUniversity of CaliforniaLos AngelesUSA

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