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Due to the characteristics of uncertainty, multivariate, strong coupling, and non-linearity in the marine alkaline protease MP fermentation process, the realization of high-performance decoupling control has always been the goal. In this thesis, a nonlinear robust decoupling control method based on artificial bee colony algorithm and multiple least squares support vector machine inversion is proposed, which combines the inverse system method with support vector machine theory. First, the “gray box” dynamics model of marine alkaline protease MP fermentation process is developed based on the material balance relationship of the fermentation process, and the existence of the inverse system is analyzed. Second, the inverse model is constructed off-line using least squares support vector machine, and the inverse model is corrected on-line by artificial bee colony algorithm according to the model deviation. Finally, the inverse model is connected in series with the original system to form a combining pseudo-linear system, and according to the characteristics of the pseudo-linear system, the internal model controller is introduced to perform closed-loop control on the system. The simulation results show that the control method can realize dynamic decoupling control of marine alkaline protease fermentation process, and the system has strong parameter robustness and good anti-interference ability.

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A Robust Decoupling Control Method Based on Artificial Bee Colony-Multiple Least Squares Support Vector Machine Inversion for Marine Alkaline Protease MP Fermentation Process