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

• Main Authors: Baruch, I. S., Hernandez, S. M., Echeverria, S. E., Castillo, O.
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptation models; Aerospace electronics; anaerobic digestion wastewater bioprocess plant; Approximation methods; Control systems; decentralized direct adaptive fuzzy-neural control with I-term; decentralized indirect fuzzy-neural sliding mode control; distributed parameter system; hierarchical fuzzy-neural multi model identifier; Learning systems; Levenberg-Marquardt learning; Takagi-Sugeno fuzzy rules with recurrent neural network antecedent parts; Topology; Vectors
• DOI: 10.1109/NAFIPS.2012.6291050

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.