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On Prediction Generation in Efficient MPC Algorithms Based on Fuzzy Hammerstein Models

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Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

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Abstract

In the paper a novel method of prediction generation, based on fuzzy Hammerstein models, is proposed. Using this method one obtains the prediction described by analytical formulas. The prediction has such a form that the MPC (Model Predictive Control) algorithm utilizing it can be formulated as a numerically efficient quadratic optimization problem. At the same time, the algorithm offers practically the same performance as the MPC algorithm in which a nonlinear, non–convex optimization problem must be solved at each iteration. It is demonstrated in the control system of the distillation column – a nonlinear control plant with significant time delay.

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References

  1. Babuska, R., te Braake, H.A.B., van Can, H.J.L., Krijgsman, A.J., Verbruggen, H.B.: Comparison of intelligent control schemes for real–time pressure control. Control Engineering Practice 4, 1585–1592 (1996)

    Article  Google Scholar 

  2. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Heidelberg (1999)

    Google Scholar 

  3. Fink, A., Fischer, M., Nelles, O., Isermann, R.: Supervision of nonlinear adaptive controllers based on fuzzy models. Control Engineering Practice 8, 1093–1105 (2000)

    Article  Google Scholar 

  4. Janczak, A.: Identification of nonlinear systems using neural networks and polynomial models: a block–oriented approach. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  5. Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Harlow (2002)

    Google Scholar 

  6. Marusak, P.: Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors. Applied Soft Computing 9, 1111–1125 (2009)

    Article  Google Scholar 

  7. Marusak, P.: Efficient model predictive control algorithm with fuzzy approximations of nonlinear models. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 448–457. Springer, Heidelberg (2009)

    Google Scholar 

  8. Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)

    Article  Google Scholar 

  9. Piegat, A.: Fuzzy Modeling and Control. Physica–Verlag, Berlin (2001)

    MATH  Google Scholar 

  10. Rossiter, J.A.: Model-Based Predictive Control. CRC Press, Boca Raton (2003)

    Google Scholar 

  11. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Systems, Man and Cybernetics 15, 116–132 (1985)

    MATH  Google Scholar 

  12. Tatjewski, P.: Advanced Control of Industrial Processes; Structures and Algorithms. Springer, London (2007)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00–665, Warszawa, Poland

    Piotr M. Marusak

Authors
  1. Piotr M. Marusak
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Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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© 2010 Springer-Verlag Berlin Heidelberg

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Marusak, P.M. (2010). On Prediction Generation in Efficient MPC Algorithms Based on Fuzzy Hammerstein Models. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-13208-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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