Abstract
The paper proposed to use a Recurrent Neural Network Model (RNNM) for centralized modeling, identification and direct adaptive control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points plus the recirculation tank. The RNNM learning algorithm is the dynamic backpropagation one. The graphical simulation results of the distributed plant direct and indirect adaptive neural control system, exhibited good convergence and precise reference tracking, outperforming the optimal control.
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Baruch, I.S., Galvan-Guerra, R. (2009). Centralized Direct and Indirect Neural Control of Distributed Parameter Systems. In: Castillo, O., Pedrycz, W., Kacprzyk, J. (eds) Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control. Studies in Computational Intelligence, vol 257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04514-1_5
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DOI: https://doi.org/10.1007/978-3-642-04514-1_5
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