An Integrated Intelligent Control Algorithm for High-Speed Train ATO Systems Based on Running Conditions

Abstract

An integrated intelligent control system is studied in this paper, which is applied to the high-speed train ATO (Automatic Train Operation) systems. According to the actual running conditions of the train, a set of fuzzy neural network controllers is proposed aiming at improving speed adjustment, riding comfort of passengers and accuracy of the stopgap. An expert decision-making system based on the operators' experience is used here for selecting the appropriate controller working on the control loop in accordance with the running condition reasoning from the train's current speed, acceleration, and location. The simulation results prove the effectiveness of this intelligent system.