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Pre-identification for Real-Time Control

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Computer Aided Systems Theory - EUROCAST 2009 (EUROCAST 2009)

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Abstract

The paper deals with the algorithm named as pre-identification, which denotes the simple general identification algorithm used for the system identification. The identification is realized before the system is controlled. It can be used in case the controlled system is time-invariant or slightly time-variant. Furthermore, the identified system might be nonlinear. Pre-identification provides a priori system description which is necessary for switching self-tuning control or useful for nonlinear control. The verification of the pre-identification usefulness was realized on several laboratory apparatuses in real-time using PC.

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References

  1. Bobal, V., Böhm, J., Fessl, J., Machacek, J.: Digital Self-tuning Controllers. Springer, London (2005)

    Google Scholar 

  2. Campi, M.C., Weyer, E.: Guaranteed non-asymptotic confidence regions in system identification. Automatica 41, 1751–1764 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Chapman, M.J., Godfrey, K.R., Chappell, M.J., Evans, E.D.: Structural identifiability for a class of non-linear compartmental systems using linear/non-linear splitting and symbolic computation. Mathematical Biosciences 183, 1–14 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  4. Gerencsér, L.: Rate of convergence of the LMS method. Systems & Control Letters 24, 385–388 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  5. Goodwin, G.C., Agüero, J.C., Welsh, J.S., Yuz, J.I., Adams, G.J.: Robust identification of process models from data. Journal of Process Control 18, 810–820 (2008)

    Article  Google Scholar 

  6. Hassaine, Y., Delourme, B., Panciatici, P., Walter, E.: M-Arctan estimator based on the trust-region method. International Journal of Electrical Power and Energy Systems 28, 590–598 (2006)

    Article  Google Scholar 

  7. Hildebrand, R., Solari, G.: Identification for control: Optimal input intended to identify a minimum variance controller. Automatica 43, 758–767 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hjalmarsson, H., Ninness, B.: Least-squares estimation of a class of frequency functions: A finite sample variance expression. Automatica 42, 589–600 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  9. Hsu, K., Novara, C., Vincent, T., Milanese, M., Poolla, K.: Parametric and nonparametric curve fitting. Automatica 42, 1869–1873 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  10. Jaulin, L.: Nonlinear bounded-error state estimation of continuous-time systems. Automatica 38, 1079–1082 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Lind, I., Ljung, L.: Regressor and structure selection in NARX models using a structured ANOVA approach. Automatica 44, 383–395 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  12. Mahata, K., Garnier, H.: Identification of continuous-time errors-in-variables models. Automatica 42, 1477–1490 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  13. Mišković, L., Karimi, A., Bonvin, D., Gevers, M.: Closed-loop identification of multivariable systems: With or without excitation of all references? Automatica 44, 2048–2056 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  14. Perutka, K.: Decentralized Adaptive Control, thesis. Tomas Bata University, Zlin, Czech Republic (2007)

    Google Scholar 

  15. Reinelt, W., Garulli, A., Ljung, L.: Comparing different approaches to model error modeling in robust identification. Automatica 38, 787–803 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  16. Romijn, R., Özkan, L., Weiland, S., Ludlage, J., Marquardt, W.: A grey-box modeling approach for the reduction of nonlinear systems. Journal of Process Control 18, 906–914 (2008)

    Article  Google Scholar 

  17. Schoukens, J., Widanage, W.D., Godfrey, K.R., Pintelon, R.: Initial estimates for the dynamics of a Hammerstein system. Automatica 43, 1296–1301 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Söderström, T.: Errors-in-variables methods in system identification. Automatica 43, 939–958 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang, L.Y., Yin, G.G.: Asymptotically efficient parameter estimation using quantized output observations. Automatica 43, 1178–1191 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  20. Yang, Z.-J., Iemura, H., Kanae, S., Wada, K.: Identification of continuous-time systems with multiple unknown time delays by global nonlinear least-squares and instrumental variable methods. Automatica 43, 1257–1264 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Perutka, K. (2009). Pre-identification for Real-Time Control. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04772-5_81

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  • DOI: https://doi.org/10.1007/978-3-642-04772-5_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04771-8

  • Online ISBN: 978-3-642-04772-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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