Abstract
In this paper, we present an adaptive extremum seeking control scheme for continuous stirred tank bioreactors. The proposed adaptive extremum seeking approach utilizes the structure information of the kinetics of the bioreactors to construct a seeking algorithm that drives the system states to the desired set-points that extremize the value of an objective function. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Simulation experiment is given to show the effectiveness of the proposed approach.
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