摘要
构建了基于PLC和组态技术的分布式监控系统的硬件平台,设计开发了下位机PLC的监控程序和基于WinCC组态环境下上位机的监测程序。针对矿井通风系统具有惯性大、时滞大、非线性等特点,提出了基于BP神经网络的PID控制方案,设计了神经网络自适应PID控制器,仿真结果表明,该方法可提高控制系统的实时性、适应性和鲁棒性。对提高矿井通风机运行的安全性和控制的准确性,保证煤矿安全高效生产具有重要的现实意义。
We established a the hardware platform of distributed monitoring and control system based on PLC and co computer PLC nfiguration technology, developed the monitoring and control program of subordinate and environment. Aimming monitoring program of principal computer based on WinCC configuration at characteristics of the mine ventilator system such as great inertia, long time- delay and nonlinearity, the PID control scheme based on BP neural network was proposed, the neural networ k PID adaptive controller was designed. The simulation results showed that the method can improve the adaptability and robustness of the control system. This study can enhance safe running of mine ventilator and accuracy of the control, It can also ensure safe and effective production in coal mine.
出处
《煤矿机械》
北大核心
2013年第10期227-229,共3页
Coal Mine Machinery
基金
陕西省科技统筹创新工程计划项目(2012KTCL01-02)
关键词
通风机
监控
BP神经网络
PID
ventilator
monitoring and control
BP neural network
PID
