In this paper the control of a bioprocess using an adaptive type-2 fuzzy logic controller is proposed. The process is concerned with the aerobic alcoholic fermentation for the growth of Saccharomyces Cerevisiae a n d i s characterized by nonlinearity and parameter uncertainty. Three type-2 fuzzy controllers heve been developed and tested by simulation: a simple type-2 fuzzy logic controller with 49 rules; a type-2 fuzzyneuro-predictive controller (T2FNPC); a type-2 selftuning fuzzy controller (T2STFC). The T2FNPC combines the capability of the type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the non linear system. In the T2STFC the output scaling factor is adjusted on-line by fuzzy rules according to the current trend of the controlled process. The advantage of the proposed adaptive algorithms is to greatly decrease the number of rules needed for the control reducing the computational load and at same time assuring a robust control.

Type-2 Fuzzy Control of a Bioreactor

Cosenza B;
2009-01-01

Abstract

In this paper the control of a bioprocess using an adaptive type-2 fuzzy logic controller is proposed. The process is concerned with the aerobic alcoholic fermentation for the growth of Saccharomyces Cerevisiae a n d i s characterized by nonlinearity and parameter uncertainty. Three type-2 fuzzy controllers heve been developed and tested by simulation: a simple type-2 fuzzy logic controller with 49 rules; a type-2 fuzzyneuro-predictive controller (T2FNPC); a type-2 selftuning fuzzy controller (T2STFC). The T2FNPC combines the capability of the type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the non linear system. In the T2STFC the output scaling factor is adjusted on-line by fuzzy rules according to the current trend of the controlled process. The advantage of the proposed adaptive algorithms is to greatly decrease the number of rules needed for the control reducing the computational load and at same time assuring a robust control.
2009
978-1-4244-4738-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1179766
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