Decoupling control of multivariable biologic fermentation process

Based on neural network inverse system a decoupling control strategy for multivariable fermentation process is proposed, in which the inverse system linearizing method of the nonlinear system is combined with the nonlinear identifying technology of the neural networks. When adequate prior information concerning the dynamics of the fermentation processes is not available and parameters of the processes change with time, the good control performance can be obtained by using the proposed strategy. The simulation experiments demonstrate that the presented decoupling approach is more effective than inverse system method based on differential geometry, because in inverse system method the design of the controller relies on the exact process model and its control performance is sensitive to the parameters of the model, which results in low accuracy and poor robust.