The Design Of Control System On Firefight Training System In Automated People Mover Systems At Airports

With the high-speed development of civil aviation, the number of passengers at airport increases dramatically. In order to improve the efficiency of airport, major airports begin to bring in Automated People Mover system. While APM system brings convenience, it also causes a lot of security issues. Because the particularity of APM system operating area, it is more sensitive to the security problem. When the fire accident inevitably broke out in APM,the firemen of airport must take efficient emergency rescue operation. The efficient emergency relief operation needs all firemen have extensive firefight experience, which comes from the actual combat. But the probability of a fire in APM system is very small,there is very little chance to get practical training. Therefore, it is necessary to design an APM fire training system. This system can provide fire simulation environment for firemen during their fire training process. The main purpose of this paper is designing a control system of the APM fire training system.This article mainly design a control system for the airport APM fire training system. The control system adopts three layers of equipment and two kinds of network to realize its control function. Using the configuration technology under the environment of KingView to design the control system of upper machine. The lower computer of the APM fire training control system consists of programmable logic controller, which is used for controlling burning plate, the fuel gas supply pipe and other bottom equipment. The training system provide a real fire training environment to fireman and improve their training efficiency. This system can also be used for training passengers to enhance their escape skills when the APM broke out fire.This paper uses BP-neural network PID controller to control the scene of the fire. The valve opening of gas supply pipeline depends on the real-time temperature of the fire scene.This paper has done an in-depth research on BP-neural network algorithm, improving the connection weights between the error back propagation neural node adjustment methods,making BP-neural network PID control algorithm a better performance.