Multivariable predictive control of a pressurized tank using neural networks |
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Authors: | Manuel A. Duarte-Mermoud Alejandro M. Suárez Danilo F. Bassi |
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Affiliation: | (1) Department of Electrical Engineering, University of Chile, Av Tupper 2007, Casilla 412-3, Santiago, Chile;(2) Department of Electrical Engineering, Federico Santa María Technical University, A. España 1680, Casilla 110-V, Valparaíso, Chile;(3) Department of Computer Science, University of Santiago of Chile, Av Ecuador 3659, Casilla 10233, Santiago, Chile |
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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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Keywords: | Predictive neural control Control of pressurized tank Multivariable control Neural control Adaptive neural control Adaptive neural network control |
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