Online neural network inverse controller in biological fermentation process and construction method of controller

Abstract

The invention discloses an online neural network inverse controller in a biological fermentation process. The controller comprises a neural network inverse offline decoupling controller construction module and a neural network inverse online learning module. The control method of the controller comprises the following steps of: connecting an offline trained neural network inverse system and a fermentation system in series to realize the linear decoupling of the system; and realizing the online update by the online learning of the neural network inverse online learning module. A neural network inverse system decoupling method and an inverse system online identification method are combined to realize the decoupling control of a multivariable fermentation system. A neural network inverse system technology is adopted to realize the decoupling of the biological fermentation system without relying on the model and parameters of the process. The inverse controller leaning module is designed based on neural network inverse decoupling, so that the controller can be adapted to the time-varying characteristics of parameters of the biological fermentation system, and thus the real-time control of variables in the fermentation process can be realized, and the controller meets the needs of practical engineering application.

Description

technical field [0001] The invention relates to an online decoupling controller for a biological fermentation process and a construction method thereof, belonging to the technical field of optimal control of a biological fermentation process. Background technique [0002] Biological fermentation system is a kind of multi-input and multi-output nonlinear system with time-varying, uncertain and strong coupling. Traditional nonlinear control methods are difficult to achieve optimal control of process variables. For nonlinear systems with multiple inputs and multiple outputs, a certain rule is generally used to linearize the system into multiple linear subsystems, and then a closed-loop linear controller is designed for each subsystem to achieve the purpose of decoupling control. The neural network inverse control method, which combines the inverse system method with the neural network method, does not depend on the precise model of the system. It only needs to know the relative...

AI Technical Summary

Problems solved by technology

Insufficient offline training of the neural network or changes in system parameters during the control process will lead to a decline in the generalization ability of the neural network, thereby degrading the closed-loop control performance

If the parameters of the fermentation process change, retraining the neural network cannot meet the real-time performance of process control

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