Automatic volume control of underground local ventilator based on nonlinear state observer

Because most of the traditional ventilator air volume control methods rely on manual experience and fixed parameter setting, it is difficult to realize the real-time matching of air volume and air demand, which leads to high energy consumption and poor ventilation effect. The automatic control of underground local ventilator based on nonlinear state observer is proposed. Elman neural network is used to predict the local air demand in underground coal mine, and the actual air supply volume of local ventilator is estimated based on the nonlinear state observer. A PID controller is designed to input the error between the air demand and the actual supply volume, and the output control signal dynamically adjusts the downhole local ventilator speed to realize automatic control of the variable air volume. The experimental results show that the design method is superior in control efficiency, stability and power consumption, which can meet the actual control requirements of the underground local fan.