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.

Keywords

Associative memory RBF network MRAC Modeling TS fuzzy model CSTR Environment improvement 

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Process EngineeringUniversity of Constantine 3ConstantineAlgeria
  2. 2.Constantine 3 University, Ecole Nationale Polytechnique de ConstantineConstantineAlgeria

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