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A multi-model predictive control method for the Pichia pastoris fermentation process based on relative error weighting algorithm
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DOI：10.1016/j.aej.2022.03.004.png)
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The Pichia pastoris fermentation process is with highly nonlinear and time-varying and strong coupling characteristics, traditional control methods such as classic PID cannot satisfy the actual needs of the fermentation process control. In order to effectively solve the control problem of the Pichia pastoris fermentation process, a multi-model predictive control method is presented based on the relative error weighting algorithm. First, the prior sample data are divided into multiple training sample sets (sample cluster) by fuzzy C-means clustering algorithm (FCM). Then, the corresponding sub prediction model is obtained by using least square support vector machine (LSSVM) and Improved particle swarm optimization (IPSO) algorithm for each sample cluster. Finally, the control strategy of predictive model is constructed based on multi model relative error weighting algorithm. The simulation in the Pichia pastoris fermentation process shows that the algorithm proposed in this paper could improve the transient response and perform good output tracking in a wide range. It improves the adaptive ability of the model and makes it more accurate to describe the actual state of the nonlinear system. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
Keyword:
Fuzzy c-means clustering
Model fusion
Multi-model predictive con-
Pichia pastoris
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