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

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.