Abstract
Abstract Microorganism culture is highly complex due to the different metabolic pathways, which are very complex. A metabolic phenomenon called overflow is a challenge to overcome in automatic control tasks of microorganism cultures. In this study, a nonlinear algorithm by sliding modes (sliding mode nonlinear control, SMNC) is proposed for the robust regulation of a fed-batch bioreactor in the presence of parametric and system perturbations. A control design is obtained using Lyapunov functions by techniques to propose a control law such that it is robust, only the output signals (biomass and volume) are used, and the reaction rates do not have to be wholly known. Therefore, a novel and simple controller capable of solving the above problems is obtained.
Highlights
Microorganism culture is highly complex due to the different metabolic pathways, which are very complex
To compare the resulting dynamics due to the input of the three different control signals, we propose to compare them concerning the calculation of the integral-squared error (ISE)
The resulting controller has a simple structure when compared to other controllers suggested in the literature since it only occupies the knowledge of the output, biomass, and volume, which stimulates its use in an accurate operation, and shows promising results in the presence of white noise
Summary
Abstract: Microorganism culture is highly complex due to the different metabolic pathways, which are very complex. In bioreactors with high-density culture, the limitations to oxygen transfer mean that the microorganism must use alternative metabolic pathways to obtain the energy necessary for growth and its maintenance due to the low solubility of oxygen the intense competition that exists for it [8]. For the automatic control theory, the operation of this type of bioreactors in high-density cultures is a problematic challenge with conventional tools, even with more modern tools such as adaptive, output interval feed back control, etc., face problems due to the large uncertainties commonly encountered.
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