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 t y p e -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. T h e 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, Bartolomeo;
2009-01-01
Abstract
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 t y p e -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. T h e 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
