Biological fermentation adaptive control system and control method based on neural network inverse

Abstract

The invention provides a biological fermentation adaptive control system and a control method based on neural network inverse. The control method comprises the following steps: constructing the neural network inverse by using a neural network and a time division multiplexing time delay link, enabling the neural network to achieve an inverse system function of a biological fermentation system by adjusting the weight coefficient of the neural network, connecting the neural network inverse and the biological fermentation system in series to form a pseudo-linear compound system, decoupling the pseudo-linear compound system in a linearization mode into two pseudo-linear subsystems with single input and single output, presetting desired signals, according to the control inputs of each pseudo-linear subsystem and the outputs of the biological fermentation system of current time and prior moment, constantly renewing the parameters of a model-free adaptive controller and the control input of each pseudo-linear subsystem of the following moment, enabling the outputs of the biological fermentation system to follow the preset desired signals effectively, thereby achieving the model-free adaptive control of the biological fermentation system, and improving the control performance of a biological fermentation process.

Description

technical field [0001] The invention relates to the control field of biological fermentation process, in particular to a model-free adaptive control system and control method based on neural network inverse in the biological fermentation process. Background technique [0002] Biological fermentation plays an increasingly important role in the fields of food, medicine, and chemical industry. The biological fermentation process is a reaction process using a specific microorganism under pure culture conditions. It involves the growth of microorganisms and is affected by operating conditions and Influenced by many factors such as the environment, the growth and metabolism of microorganisms depends on multiple process variables, and different process variables have different effects on the growth of microorganisms and the formation of products. In order to achieve the optimal trajectory of microbial growth, ensure the quality of fermentation products, increase product yield, redu...

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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