微压富氧舱系统建模及控制算法研究  

Research on Modeling and Control Algorithm of Micro-Pressure and Oxygen-Enriched Cabin

作　　者：刘柱 卢剑锋[1] 王一达 李家春[1] LIU Zhu;LU Jian-feng;WANG Yi-da;LI Jia-chun(School of Mechanical Engineering,Guizhou University,Guizhou Guiyang 550025,China)

机构地区：[1]贵州大学机械工程学院,贵州贵阳550025

出　　处：《机械设计与制造》2020年第12期167-170,175,共5页Machinery Design & Manufacture

基　　金：贵州省科技厅项目—车辆动载荷模拟超大型实验舱体关键技术研发(黔科合支撑[2017]2006)。

摘　　要：为提高微压富氧舱压力升降速度和氧浓度控制效果,通过建立了系统压力系统数学模型和氧浓度数学模型,分析氧浓度与压力变化之间的关系,提出了系统控制策略。运用Matlab/simulink进行控制仿真,对比PID与BP神经网络PID氧浓度控制效果;采用PI追踪的方法控制压力升降。仿真表明BP神经网络PID比PID氧浓度控制准确,PI控制能良好控制压力升降。通过OPC技术实现WINCC与Matlab数据交换,实现BP神经网络PID氧浓度控制。实验表明控制策略可行,能准确控制压力升降,维持舱内目标氧气浓度。In order to improve the pressure rise and fall speed and oxygen concentration control effect of the micro-pressure and oxygen-enriched cabin.Established system pressure mathematical model and oxygen concentration mathematical model to analyze the relationship between oxygen concentration and pressure changes.Using Matlab/simulink for control simulation,comparing PID and BP neural network and PID oxygen concentration control effect.Using the PI tracking to control the pressure rise and fall.The simulation shows that the BP neural network is more accurate than the PID oxygen concentration control,and PI control can control the pressure rise and fall well.Through OPC technology to achieve WINCC and Matlab data exchange and BP neural network PID oxygen concentration control.Experiments show that the control strategy is feasible and can control the pressure rise and fall accurately and maintain the target oxygen concentration in the cabin.

关 键 词：微压富氧 数学模型 BP神经网络PID OPC技术 

分 类 号：TH16[机械工程—机械制造及自动化] TH49