Bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying, in particular, when they are operating in fed batch mode. For this kind of bioprocess where the mathematical model contains many structured and unstructured uncertainties, we try to combine different intelligent techniques based on natural syllogisms of these techniques. In order to obtain a high bioprocess productivity it is essential to accord the benefits of the classical control strategy (i.e. the analytical determination of the optimum) with the subjective bioprocess characterization (due to the human expert) in order to diminish the on line information scarcity. The research objective of this study was to develop an appropriate control method for a new complex bioprocess and to implement it on a laboratory plant. Hence, an intelligent control structure has been designed in order to produce biomass and to maximize the specific growth rate.