Decentralized indirect adaptive Fuzzy-Neural Multi-Model control of a distributed parameter bioprocess plant

The paper proposed to use recurrent fuzzy-neural multi-model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation tank), which are used as centers 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 implemented by a hierarchical fuzzy-neural multi-model sliding mode controller (HFNMM-SMC). The comparative graphical simulation results of the digestion wastewater treatment system identification and control, obtained via learning, exhibited a good convergence, and precise reference tracking out performing the optimal control.