Decentralized direct and indirect I-term adaptive fuzzy-neural control of a bioprocess plant

The paper proposed to use a recurrent neural network model, and a real-time Levenberg-Marquardt algorithm of its learning for decentralized fuzzy-neural data-based modeling, identification and 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, used as process data generator, represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points plus one - in the recirculation tank. The paper proposed to use direct adaptive integral plus states fuzzy-neural control, and indirect adaptive I-term sliding mode fuzzy-neural control. The comparative graphical simulation results of the digestion wastewater treatment system control, exhibited a good convergence and precise reference tracking, giving slight priority to the direct control with respect to the indirect control applied.