Decentralized Adaptive Fuzzy-Neural Control of an Anaerobic Digestion 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 used as a plant data generator. It 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 used to design hierarchical FNMM direct and indirect controllers. The comparative graphical simulation results of the digestion system direct and indirect control, obtained via learning, exhibited a good convergence, and precise reference tracking. The comparative numerical results, giving the final means squared error of control of each output variable showed that the indirect adaptive decentralized fuzzy-neural control outperformed the direct one, and the it outperformed the linearized proportional optimal control too. Keywords— Decentralized control, direct adaptive control, indirect adaptive control, distributed parameter digestion bioprocess system, recurrent neural network model, hierarchical fuzzy neural identification and control.

[1]  M. Willis,et al.  ADVANCED PROCESS CONTROL , 2005 .

[2]  Ioannis G. Kevrekidis,et al.  Identification of distributed parameter systems: A neural net based approach , 1998 .

[3]  Guanrong Chen,et al.  Spectral-approximation-based intelligent modeling for distributed thermal processes , 2005, IEEE Transactions on Control Systems Technology.

[4]  Jovan D. Bošković,et al.  Comparison of linear, nonlinear and neural-network-based adaptive controllers for a class of fed-batch fermentation processes , 1995, Autom..

[5]  Ieroham S. Baruch,et al.  A Sliding Mode Control Using Fuzzy-Neural Hierarchical Multi-model Identifier , 2007, IFSA.

[6]  Abhay B. Bulsari,et al.  Application of neural networks for system identification of an adsorption column , 1993, Neural Computing & Applications.

[7]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Ieroham S. Baruch,et al.  A Fuzzy-Neural Hierarchical Multi-model for Systems Identification and Direct Adaptive Control , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[9]  R. Garrido,et al.  A fuzzy neural recurrent multi-model for systems identification and control , 2001, 2001 European Control Conference (ECC).

[10]  Ieroham S. Baruch,et al.  A fuzzy-neural multi-model for nonlinear systems identification and control , 2008, Fuzzy Sets Syst..

[11]  Radhakant Padhi,et al.  Proper orthogonal decomposition based optimal neurocontrol synthesis of a chemical reactor process using approximate dynamic programming , 2003, Neural Networks.

[12]  G. Fairweather,et al.  Orthogonal spline collocation methods for partial di erential equations , 2001 .

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[15]  Han-Xiong Li,et al.  Hybrid intelligence based modeling for nonlinear distributed parameter process with applications to the curing process , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[16]  Radhakant Padhi,et al.  Adaptive-critic based optimal neuro control synthesis for distributed parameter systems , 2001, Autom..