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Multivariable predictive control of a pressurized tank using neural networks

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Abstract

The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.

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Acknowledgments

The research contained in this paper was supported by CONICYT under grants FONDECYT 1970351 and FONDECYT 1980361.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Chile, Av Tupper 2007, Casilla 412-3, Santiago, Chile

    Manuel A. Duarte-Mermoud

  2. Department of Electrical Engineering, Federico Santa María Technical University, A. España 1680, Casilla 110-V, Valparaíso, Chile

    Alejandro M. Suárez

  3. Department of Computer Science, University of Santiago of Chile, Av Ecuador 3659, Casilla 10233, Santiago, Chile

    Danilo F. Bassi

Authors
  1. Manuel A. Duarte-Mermoud
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  2. Alejandro M. Suárez
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  3. Danilo F. Bassi
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Corresponding author

Correspondence to Manuel A. Duarte-Mermoud.

Appendix

Appendix

Table 1 The following are the numerical values of the model parameters used in all simulations
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Duarte-Mermoud, M.A., Suárez, A.M. & Bassi, D.F. Multivariable predictive control of a pressurized tank using neural networks. Neural Comput & Applic 15, 18–25 (2006). https://doi.org/10.1007/s00521-005-0003-0

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