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Fuzzy modelling and model reference neural adaptive control of the concentration in a chemical reactor (CSTR)

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Abstract

This simulation study is a fuzzy model-based neural network control method. The basic idea is to consider the application of a special type of neural networks based on radial basis function, which belongs to a class of associative memory neural networks. The novelty of this approach is the use of an RBF neural network controller in a model reference adaptive control architecture, based on a one-step-ahead Takagi–Sugeno fuzzy model. The objective is to control the concentration in a continuous stirred-tank reactor highly non-linear system and to assure its stability by limiting the temperature rise generated from the irreversible exothermic reaction. This contribution will help to reduce environmental impact of chemical waste.

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Authors and Affiliations

  1. Faculty of Process Engineering, University of Constantine 3, 25000, Constantine, Algeria

    M. Bahita

  2. Constantine 3 University, Ecole Nationale Polytechnique de Constantine, 25000, Constantine, Algeria

    K. Belarbi

Authors
  1. M. Bahita
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  2. K. Belarbi
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Correspondence to M. Bahita.

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Bahita, M., Belarbi, K. Fuzzy modelling and model reference neural adaptive control of the concentration in a chemical reactor (CSTR). AI & Soc 33, 189–196 (2018). https://doi.org/10.1007/s00146-018-0806-z

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  • DOI: https://doi.org/10.1007/s00146-018-0806-z

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