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Generalized predictive control based on neural networks

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Abstract

This paper presents the Generalized Predictive Control (GPC) strategy based on Artificial Neural Network (ANN) plant model. To obtain the step and the free process responses which are needed in the generalized predictive control strategy we iteratively use a multilayer feedforward ANN as a one-step-ahead predictor. A bioprocess was chosen as a realistic nonlinear SISO system to demonstrate the feasibility and the performance of this control scheme. A comparison was made between our approach and the adaptive GPC (AGPC).

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Authors and Affiliations

  1. Faculty of Chemical Technology, Department of Process Control, Slovak Technical University, Radlinského 9, 812 37, Bratislava, Slovak Republic

    A. Rusnák, M. Fikar & A. Mészáros

  2. E.N.S.I.G.C., Chemin de la loge, 31078, Toulouse Cedex, France

    K. Najim

Authors
  1. A. Rusnák
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  2. M. Fikar
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  3. K. Najim
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  4. A. Mészáros
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Rusnák, A., Fikar, M., Najim, K. et al. Generalized predictive control based on neural networks. Neural Process Lett 4, 107–112 (1996). https://doi.org/10.1007/BF00420619

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  • DOI: https://doi.org/10.1007/BF00420619

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