高空台特种调节阀建模方法对比研究

In order to achieve accurate flow characteristics model of the disc type control valve and improve the accuracy of inlet flow prediction of altitude cabin, modeling method based on BP neural network and NARX network is proposed. Based on the analysis of the position of the control valve and sensor point, the control valve and the pipe behind the valve are modeled as a whole. The modeling methods of the flow coefficient, static BP neural network, and dynamic NARX network based on Gamma Test (GT), are compared, and suggestions on selecting in engineering are given. Finally, based on the test flow data, the steady-state error and dynamic error of the flow output of each model are simulated and compared when the valve changes differently. The results show that the modeling precision of the BP neural network method and the NARX network method is better than the flow coefficient method. Meanwhile, the maximum steady-state error of the BP neural network model is 0.52kg/s, which is preferable to the models of NARX network and flow coefficient. Compared with the models of BP neural network and flow coefficient, the NARX network model whose maximum dynamic error is 2.04kg/s, can show the dynamic flow characteristics accurately.