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Advances in Fuzzy Control pp 129–154Cite as

Inverse Fuzzy Model Based Predictive Control

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 16))

Abstract

Conventional fuzzy control is based on expert knowledge in the form of fuzzy if-then rules. Practice shows, however, that it is often not possible to collect sufficient information to design a well-performing fuzzy controller. Human control skills are generally difficult to verbalize, since the operator’s control strategy is often based on the simultaneous use of various control principles, combining feedforward, feedback, and predictive strategies in a complex, time-varying fashion. In such a case, the operator may not be able to explain why a particular control action is chosen. Moreover, the rules provided by different operators are often contradictory.

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Author information

Authors and Affiliations

  1. Control Laboratory, Department of Electrical Engineering, Delft University of Technology, PO Box 5031, 2600 GA, Delft, The Netherlands

    R. Babuška, J. M. Sousa & H. B. Verbruggen

Authors
  1. R. Babuška
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  2. J. M. Sousa
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  3. H. B. Verbruggen
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Linköping, S-58183, Linköping, Sweden

    Dimiter Driankov

  2. Corporate Technology Information and Communications, Dept. ZT IK4, Siemens AG, D-81730, Munich, Germany

    Rainer Palm

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© 1998 Springer-Verlag Berlin Heidelberg

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Babuška, R., Sousa, J.M., Verbruggen, H.B. (1998). Inverse Fuzzy Model Based Predictive Control. In: Driankov, D., Palm, R. (eds) Advances in Fuzzy Control. Studies in Fuzziness and Soft Computing, vol 16. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1886-4_6

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  • DOI: https://doi.org/10.1007/978-3-7908-1886-4_6

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-11053-9

  • Online ISBN: 978-3-7908-1886-4

  • eBook Packages: Springer Book Archive

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