Adaptive and Robust-Adaptive Control Strategies for a Class of Fed-Batch Fermentation Processes

This paper presents the design and analysis of adaptive and robust-adaptive control strategies for a class of fed-batch fermentation processes. The control strategies are developed under the realistic assumption that both the bacterial growth rates and the influent flow rates are time-varying, and uncertain, but some lower and upper bounds of these uncertainties are known. The adaptive control structure is achieved by combining a linearizing control law with a state asymptotic observer and a parameter estimator used for on-line estimation of bioprocess unknown kinetics. The robust-adaptive control structure is achieved by combining a linearizing control law with an interval observer able to estimates a lower and an upper bound of unmeasurable states. Also, in the proposed robust-adaptive control strategy the uncertain process parameters are replaced by their lower and upper bounds assumed known. The effectiveness of the designed algorithms is validated by several numerical simulations applied to a particular alcoholic fermentation bioprocess.