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Information Management in Computer Integrated Manufacturing pp 257–288Cite as

Fuzzy modeling and control

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 973))

Abstract

The paper presents fuzzy modelling as a tool for design of nonlinear controllers. An architecture and function of a fuzzy logic controller (FLC) is described. Three implementations of FLCs are recognized: look-up table, fuzzy relational and fuzzy logic-neural network based systems. Fuzzy logic — neural networks (FLNN) are analyzed in greater details. Architectures suitable for a FLNN identification for plant and operator modelling are discussed, passive and active regimes for tuning of controller parameters are explained. Unsupervised and supervised identification techniques are described. Principles of direct inverse, model reference, internal model and optimal predictive fuzzy control are explained. Finally, commercial software and hardware tools for design, implementation and evaluation of fuzzy controllers are summarized.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Department of Control Engineering, Czech Technical University of Prague, Technická 2, 166 27, Prague 6, Czech Republic

    Petr Horáček

  2. Laboratoire d'Automatique de Grenoble, Institut National Polytechnique de Grenoble, ENSIEG, B.P. 46, 38402, Saint-Martin-d'Hères, France

    Petr Horáček

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  1. Petr Horáček
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Editor information

Heimo H. Adelsberger Jiří Lažanský Vladimír Mařík

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© 1995 Springer-Verlag Berlin Heidelberg

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Horáček, P. (1995). Fuzzy modeling and control. In: Adelsberger, H.H., Lažanský, J., Mařík, V. (eds) Information Management in Computer Integrated Manufacturing. Lecture Notes in Computer Science, vol 973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60286-0_105

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  • DOI: https://doi.org/10.1007/3-540-60286-0_105

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60286-6

  • Online ISBN: 978-3-540-44785-6

  • eBook Packages: Springer Book Archive

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