Multi-model predictive control method for pichia pastoris fermentation process

A multi-model predictive control method for a Pichia pastoris fermentation process, comprising:

• acquiring a concentration of protease K produced by Pichia pastoris fermentation;
• clustering the concentration of protease K produced by Pichia pastoris fermentation by using a fuzzy C-means algorithm (FCM), and acquiring m sample clusters;
• inputting each sample cluster into a least squares support vector machine (LSSVM) for training, and optimizing key parameters of LSSVM by using the improved particle swarm optimization (IPSO), and establishing m optimal sub-prediction models FCM-IPSO-LSSVM1-FCM-IPSO-LSSVMm;
• designing a corresponding model predictive controller for each optimal sub-prediction model;
• acquiring a current state of a system;
• calculating an output of the m optimal sub-prediction models and a mean-square error (MSE) Ri(k) of controlled objects, and selecting an optimal sub-prediction model with a minimum MSE as a matching model;
• calculating a weight wi of each sub-model predictive controller according to the MSE Ri(k);
• weighting and summing m sub-model predictive controllers according to a weight wi of m model predictive controllers, and taking a fusion controller as a control input to construct a multi-model fusion predictive controller;
• and
• controlling the Pichia pastoris fermentation process by the multi-model fusion predictive controller.