An Automatic Train Operation Strategy Based on Prescribed Performance Control

With the fast development of 5G communications, big data and intelligent control technologies, automatic train operation (ATO) is expected to supersede traditional manual driving for high-speed railway systems. To study the precise automatic control algorithm for high-speed trains (HST), a novel prescribed performance tracking control method based on the barrier Lyapunov function (BLF) is proposed in this paper. First, the dynamic model of HST with basic and additional resistances is established considering actual operating condition and actuator saturation phenomenon. Second, an auxiliary system is constructed to eliminate the negative effect caused by input saturation, and a prescribed performance tracking controller with adaptive law is designed for ATO system. Finally, the stability of the close-loop system and the boundedness of the tracking error signals are proved based on Lyapunov stability theorem. Simulation results demonstrate the effectiveness of the proposed controller.