Decoupling Control of the Gas Collectors’ Pressure System Based on Neural Network Inverse System
Fan Meng, Qing Tian, Bin Xu
Abstract
Fan Meng, Qing Tian, Bin Xu
Abstract
For collectors’ pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors’ pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors’ pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors’ pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.
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For collectors’ pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors’ pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors’ pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors’ pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.
Key concepts: Decoupling (probability), Inverse system, Inverse, Artificial neural network, Nonlinear system, Control theory (sociology), Pressure system, Control system