Sliding mode control of a hydrocarbon degradation in biopile system using recurrent neural network model

Authors:
Ieroham Baruch;Carlos-Roman Mariaca-Gaspar;Israel Cruz-Vega;Josefina Barrera-Cortes
Affiliations:
CINVESTAV-IPN, Mexico D.F., Mexico;CINVESTAV-IPN, Mexico D.F., Mexico;CINVESTAV-IPN, Mexico D.F., Mexico;Department of Biotechnology and Bioengineering, Mexico D.F., Mexico
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

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Abstract

This paper proposes the use of a Recurrent Neural Network (RNN) for modeling a hydrocarbon degradation process carried out in a biopile system. The proposed RNN model represents a Kalman-like filter and it has seven inputs, five outputs and twelve neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the dynamic Backpropagation one. The obtained RNN model is simplified and used to design a Sliding Mode Control (SMC). The graphical simulation results of biopile system approximation, obtained via RNN model learning and the designed process SMC exhibited a good convergence, and precise system reference tracking.