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Systematic Intelligent Observer Design for Plants Characterized by an Isolated Nonlinearity

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Intelligent Observer and Control Design for Nonlinear Systems

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Abstract

The starting point for control design is a theoretical analysis of the plant to be controlled resulting in a mathematical model. This model can either be linear or nonlinear. In the nonlinear case, a common method for controller design is to linearize the model at certain points of operation and then to use linear control theory.

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References

  1. Anderson, B. D. O.: Exponential Stability of Linear Systems Arising from Adaptiv Identification. IEEE Transactions on Automatic Control, 22–2, 1977.

    Google Scholar 

  2. Engell, S.: (Hrsg.) Entwurf nichtlinearer Regelungen. R. Oldenbourg Verlag, München, 1995.

    Google Scholar 

  3. Frenz, Th.: Stabile neuronale online Identifikation und Kompensation statischer Nicht-linearitäten am Beispiel von Werkzeugmaschinenvorschubantrieben. Dissertation, TU München, 1997.

    Google Scholar 

  4. Gauß, C. F.: Theory of Motion of the Heavenly Bodies. New York, Dover, 1963.

    MATH  Google Scholar 

  5. Hakala, J., Koslowski, C, Eckmiller, R.: ‘Partition of Unity’ RBF Networks are Universal Function Approximators. Proceedings of the ICANN’ 94, Sorrento, Italy, Springer-Verlag, Berlin, Heidelberg, 1994, pp. 459–462.

    Google Scholar 

  6. Hangl, F., Lenz, IL, Schröder, D.: Theorie des systematischen Entwurfs lernfähiger Beobachter für eine Klassenichtlinearer Strecken. GMA-Ausschuss 1.4 Theoretische Verfahren der Regelungstechnik, Inter-laken, Switzerland, 28.9–1.10.1997.

    Google Scholar 

  7. Kaiman, R. E.: Fundamental study of adaptive control systems. Tech. Rept. ASD-TR-61, Vol. 1 (NASA N62–15355).

    Google Scholar 

  8. Kaiman, R. E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82D, 1960.

    Google Scholar 

  9. Kaiman, R. E., Buey, R. S. New results in linear filtering and prediction theory. J. Basic Eng. 83D, 1961.

    Google Scholar 

  10. Krebs, V.: Nichtlineare Filterung. Methoden der Regelungstechnik, R. Oldenbourg Verlag, München, 1980.

    Google Scholar 

  11. Lenz, IL, Schröder, D.: Local Identification using Artificial Neural Networks. Proc. Ninth Yale Workshop on Adaptive and Learning Systems, June 10–12, 1996, Yale University, CT, USA, pp 83–88.

    Google Scholar 

  12. Lenz, U., Schröder, D.: Identifikation isolierter Nichtlinearitäten mit Neuronalen Netzen. GMA-Kongress Mess- und Automatisierungstechnik 96, Baden Baden, Germany, 9.–11. September 1996.

    Google Scholar 

  13. Lenz, U., Schröder, D.: Identifikation isolierter Nichtlinearitäten mit Neuronalen Netzen. GMA-Ausschuss 1.4 Theoretische Verfahren der Regelungstechnik, Inter-laken, Switzerland, 29.9–2.10.1996.

    Google Scholar 

  14. Luenberger, D. G.: Observing the state of linear systems. IEEE Transactions on Military Electronics, 1964, pp. 74–80.

    Google Scholar 

  15. Luenberger, D. G.: Observers for multivariable systems. IEEE Transactions on Automatic Control, pp. 190–197, 1966.

    Google Scholar 

  16. Misawa, E.A., Hedrick, J.K. Nonlinear observers — A state-of-the-art survey. ASME Journal of Dynamic Systems, Measurement and Control 111, 1989, pp. 344–352.

    Article  MATH  Google Scholar 

  17. Morgan, A. P., Narandra, K. S.: On the Uniform Asymptotic Stability of Certain linear NonautonomousDifferential Equations. S.I.A.M. Journal of Control and Optimization, 15, 1977.

    Google Scholar 

  18. Narendra, K., Annaswamy, A.M.: Stable Adaptive Systems. Prentice Hall, Englewood Cliffs, New Jersey, 1989.

    MATH  Google Scholar 

  19. Narendra, K., Partasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks, Vol. 1, No. 1, March 1990, pp. 4–27.

    Article  Google Scholar 

  20. Parks, P.: Ljapunov Redesign of Model Reference Adaptive Control Systems. IEEE Transactions on Automatic Control, Vol. 11, 1966, pp. 362–367.

    Article  Google Scholar 

  21. Schaffner C: Analyse und Synthese neuronaler Regelungsverfahren. Herbert Utz Verlag Wissenschaft, München, 1996.

    Google Scholar 

  22. Schaffner, C, Schröder, D.: Stable Nonlinear Observer Design with Neural Network. IFAC-Workshop Motion Control, Munich, Germany, 1995.

    Google Scholar 

  23. Schaffner, C, Schröder, D., Lenz, U.: Application of Neural Networks to Motor Control. International Power Electronics Conference, IPEC’95, Yokohama, Japan, Proc. Vol. 1, 1995, pp 46–51.

    Google Scholar 

  24. Schroeder, D., Schaeffner, C, Lenz, IL: Neural-Net Based Observers for Sensorless Drives. 20th International Conference on Industrial Electronics, Control and Instrumentation IECON’ 94, Bologna, Italy, Proceedings, Vol. 3, 1994, pp. 1599–1610.

    Google Scholar 

  25. Schröder, D.: Elektrische Antriebstechnik 1. Springer-Verlag, Berlin, Heidelberg, 1994.

    Google Scholar 

  26. Schröder, D.: Elektrische Antriebstechnik 2, Regelung von Antrieben. Springer-Verlag, Berlin, Heidelberg, 1995.

    Google Scholar 

  27. Slotine, J., Li, W.: Applied Nonlinear Control. Prentice-Hall, Inc., Englewood Cliffs, NJ 07632, 1991.

    MATH  Google Scholar 

  28. Sorenson, H. W.: Kaiman Filtering: Theorie and Application. IEEE Press 1985.

    Google Scholar 

  29. Sorenson, H. W.: Kaiman Filtering Techniques. Advances in Control Systems Theory and Applications, Academic Press, 1966.

    Google Scholar 

  30. Specht, D.: A General Regression Neural Network. IEEE Transactions on Neural Networks, Vol. 2, No. 6, November 1991, pp. 568–576.

    Article  Google Scholar 

  31. Straub, S., Schröder, D.: Neuronalbasierter Beobachterentwurf für eine breite Klasse nichtlinearerSysteme. Fachtagung Computational Intelligence, VDI Berichte 1381, 1998, pp. 361–380,

    Google Scholar 

  32. Straub, S., Schröder, D.: Identification of Nonlinear Dynamic Systems with Recurrent Neural Networks and Kaiman Filter Methods. ISCAS’ 96, Atlanta, USA. Proceedings, Vol. 3, 1996, pp. 341–345.

    Google Scholar 

  33. Straub, S., Schröder, D.: Neural Network based Identification Methods to solve Nonlinear Problems in Rolling Mill Subsystems. 2nd International Conference on Modelling of Metal Rolling Processes, London, GB, 1996, Proceedings pp. 366–377.

    Google Scholar 

  34. Strobl, D., Lenz, U., Schröder, D.: Systematic Design of Stable Neural Observers for a Class of Nonlinear Systems. Proceedings of the 1997 IEEE International Conference on Control Applications, Hartford, Conneticut USA, October 5–7, 1997, pp. 377–382.

    Google Scholar 

  35. Tzirkel-Hancock, E., Fallside, F.: A Direct Control Method for a Class of Nonlinear Systems using Neural Networks. Technical Report CUED/F-INFENG/TR.65, Cambridge University, Engineering Departement. Cambridge, England, 1991.

    Google Scholar 

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Authors
  1. Ulrich Lenz
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Editor information

Editors and Affiliations

  1. Institute for Electrical Drive Systems, Technical University of Munich, Arcisstrasse 21, D-80333, München, Germany

    Dierk Schröder

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Lenz, U. (2000). Systematic Intelligent Observer Design for Plants Characterized by an Isolated Nonlinearity. In: Schröder, D. (eds) Intelligent Observer and Control Design for Nonlinear Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04117-8_5

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  • DOI: https://doi.org/10.1007/978-3-662-04117-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08346-4

  • Online ISBN: 978-3-662-04117-8

  • eBook Packages: Springer Book Archive

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