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A neural network model based predictive control approach: application to a semi-batch reactor

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Abstract

Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes.

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Authors and Affiliations

  1. High Institute of Technological Studies of Ksar-Hellal, 5070, Ksar Hellal, Monastir, Tunisia

    F. M’Sahli

  2. National School of Engineers of Gabes, University of Sfax, 6029, Gabes, Tunisia

    R. Matlaya

Authors
  1. F. M’Sahli
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  2. R. Matlaya
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Correspondence to F. M’Sahli.

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M’Sahli, F., Matlaya, R. A neural network model based predictive control approach: application to a semi-batch reactor. Int J Adv Manuf Technol 26, 161–168 (2005). https://doi.org/10.1007/s00170-003-1972-8

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  • DOI: https://doi.org/10.1007/s00170-003-1972-8

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