Decentralized direct I-term fuzzy-neural control of an anaerobic digestion bioprocess plant

Abstract

The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in fixed bed and recirculation tank. The distributed parameter analytical model of the digestion bioprocess is used as a plant data generator. It is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points (plus one point of the recirculation tank), which are used as centres of the membership functions of the fuzzyfied plant output variables with respect to the space variable. The local and global weight parameters and states of the proposed FNMM identifier are learnt by the Levenberg-Marquardt learning algorithm and they are implemented by a Hierarchical Fuzzy-Neural Multi-Model Direct Controller with Integral Term. The graphical simulation results of the digestion system direct fuzzy-neural I-term learning control, exhibited a good convergence, and precise reference tracking.