Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and
compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the
growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity
and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive
controller (T2FNPC) that combines the capability of 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 nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI
controller, where the output scaling factor is adjusted online by fuzzy rules according to the current
trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used
as reference
