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Disturbance Measurement Utilization in Easily Reconfigurable Fuzzy Predictive Controllers: Sensor Fault Tolerance and Other Benefits

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Book cover Rough Sets and Current Trends in Computing (RSCTC 2010)

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Abstract

The easily reconfigurable predictive controllers are supplemented with a mechanism of disturbance measurement utilization. It is done in such a way that the main advantage of the controllers – their simplicity – is maintained. The predictive controllers under consideration are based on fuzzy Takagi–Sugeno (TS) models in which step responses are used as local models. These models are supplemented with the parts describing the influence of disturbances on the outputs of the control plant. Then, the controllers are formulated in such a way that the control signals are easily generated. Efficiency and usefulness of the predictive controllers utilizing disturbance measurement is demonstrated in the example control system of a nonlinear control plant with delay.

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References

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

    Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  4. Marusak, P., Tatjewski, P.: Effective dual–mode fuzzy DMC algorithms with on–line quadratic optimization and guaranteed stability. International Journal of Applied Mathematics and Computer Science 19, 127–141 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  5. 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 

  6. Marusak, P.: Easily reconfigurable analytical fuzzy predictive controllers: actuator faults handling. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds.) ISICA 2008. LNCS, vol. 5370, pp. 396–405. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Piegat, A.: Fuzzy Modeling and Control. Physica-Verlag, Berlin (2001)

    MATH  Google Scholar 

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

    Google Scholar 

  9. 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 

  10. 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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Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Marcin Szczuka

  2. ICS, Warsaw University of Technology,,  

    Marzena Kryszkiewicz

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Dept. of Computer Science, The University of Wales, Aberystwyth, UK

    Richard Jensen

  5. Harbin Institute of Technology, PO Box 458, 150006, Harbin, China

    Qinghua Hu

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Marusak, P.M. (2010). Disturbance Measurement Utilization in Easily Reconfigurable Fuzzy Predictive Controllers: Sensor Fault Tolerance and Other Benefits. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_59

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  • DOI: https://doi.org/10.1007/978-3-642-13529-3_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13528-6

  • Online ISBN: 978-3-642-13529-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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