Research on Adaptive Iterative Learning Control of Air Pressure in Railway Tunnel with IOTs Data

Abstract

When a train enters a tunnel, the passengers in the train will feel tinnitus. The main reason is that the pressure in the tunnel enters the vehicle through the adjusting system of the train, which will cause discomfort to the passengers. In this paper, according to the quasi-periodicity and repeatability of mass data in the process of train running in tunnels, a control method based on the IOTs big data is proposed, and an adaptive iterative learning control algorithm based on the IOTs big data is established. The fan operation frequency of ventilation system is regulated by adaptive iterative learning control algorithm, and can adjust the new air and exhaust gas of the ventilation system in real time to restrain the pressure fluctuation in the train. Finally, the simulation results show that the adaptive iterative learning control algorithm based on the Internet of Things can significantly reduce the amplitude of pressure fluctuation in the tunnel and the change rate of the ventilator, and improve the passenger comfort program. Moreover, the real-time measured data also show that the proposed closed-loop adaptive iterative learning control algorithm based on the Internet of Things is obviously superior to active control.