, Volume 33, Issue 2, pp 189–196 | Cite as

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

  • M. Bahita
  • K. Belarbi
Original Article


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.


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


  1. Astrom KJ, Wittenmark B (1995) Adaptive control, 2nd edn. Addison-Wesley, New JerseyGoogle Scholar
  2. Bahita M, Belarbi K (2012) Fuzzy and neural adaptive control of a class of nonlinear systems, LAP LAMBERT Academic Publishing GmbH & Co. KG Heinrich-Böcking-Str. 6–8, 66121, Saarbrücken, Germany, ISBN: 978-3-8484-8920-6Google Scholar
  3. Bahita M, Belarbi K (2014) On-line Neural Network, Adaptive Control of a Class of Nonlinear Systems Using Fuzzy Inference Reasoning. Rev Roum Sci Techn- Electrotech Et Energ 59:401–410Google Scholar
  4. Bahita M, Belarbi K (2015) Fuzzy adaptive control of dissolved oxygen in a waste water treatment process. IFAC-PapersOnLine 48(24):66–70CrossRefGoogle Scholar
  5. Darken C, Moody J (1990) Fast Adaptive k-means Clustering: Some empirical Results. Int Joint Conf Neural Networks 2:233–238Google Scholar
  6. Du KL, Swamy MNS (2014) Neural networks and statistical learning. Springer, LondonCrossRefMATHGoogle Scholar
  7. Ge SS, Hang CC and Zhang T (1998) Nonlinear adaptive control using neural networks and its application to CSTR systems. J Process Control (9):313–323Google Scholar
  8. Geng F (2006) A survey on analysis and design of model-based fuzzy control systems. IEEE Trans Fuzzy Syst 14(5):676–697CrossRefGoogle Scholar
  9. Hunt KJ, Sbarbaro D (1991). Neural networks for nonlinear internal model control. In: IEE Proceedings D—control theory and applications, Vol. 138, No. 5, pp. 431–438Google Scholar
  10. Precup RE, Hellendoorn H (2011) A survey on industrial applications of fuzzy control. Comput Ind 62:213–226CrossRefGoogle Scholar
  11. Sanner. R, M, Slotine JJE (1992) Gaussian networks for direct adaptive control. IEEE Trans Neural Networks 3(6):837–863CrossRefGoogle Scholar
  12. Sbarbaro H, Neumerkel KH (1993) Neural control of a steel rolling mill. IEEE Control Systems 13(3):69–75CrossRefGoogle Scholar
  13. Takagi T, Sugeno M (1985) Fuzzy Identification of systems and its applications to modeling and Control. IEEE Trans Syst Man Cybern SMC15(1):116–132CrossRefMATHGoogle Scholar
  14. Tzirkel E, Fallside F (1992) Stable control of nonlinear systems using neural networks. Int J Robust Nonlinear Control 2(1):63–86CrossRefMATHGoogle Scholar

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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