Multi-model predictive control method for pichia pastoris fermentation process

The invention discloses a multi-model predictive control method for a pichia pastoris fermentation process. The multi-model predictive control method comprises the following steps: dividing prior data into m training sample sets (sample clusters) by utilizing a fuzzy C-means clustering algorithm (FCM); for each sample cluster, a corresponding prediction model is obtained by adopting a least square support vector machine (LSSVM) and an improved particle swarm optimization (IPSO) method, then corresponding prediction controllers are respectively designed for m local prediction models, and finally, the deviation between the output of an object and the output of each sub-prediction model is calculated at each sampling moment, so that a prediction result is obtained. A multi-model fusion predictive controller is constructed based on a multi-model relative error weighting algorithm, and the pichia pastoris fermentation process is controlled by using the control method. According to the method, the adaptive capacity of the model is improved, so that the actual state of the nonlinear system is described more accurately.