Research Of Automatic Train Operation System Based On Grey System Theory

The speed of trains and traffic density is improving with the rapid development of Chinese railway. Under this background, traditional driving mode is difficult to meet the requirements of the train operation. In order to guarantee punctuality of the train operation, improve passenger comfort and reduce the energy consumption, more intelligent automatic train operation(ATO) system is needed to control the train. Researching the ATO system by particle swarm optimization(PSO) and grey system theory after analyzing the structure of the automatic driving system.At first, research status and achievement of algorithm being used in ATO system is illustrated and the disadvantage is pointed out. After the structure of system and function requirements of the ATO system is analyzed, combining with the property of the research object of grey theory. The ATO system is proved to possess gray characteristics and can be controlled by grey theory. After the structure of ATO system is analyzed, the speed controller is designed based on grey theory. The designed speed controller is mainly composed of three modules, grey prediction module, correction module and grey decision module. In addition, in order to satisfy the train running requirements of safety, punctuality, passenger comfort and energy consumption and other various aspects of requirements, the reference speed curve of ATO system is generated by using particle swarm optimization.Finally, simulation is performed in Matlab platform with actual data to demonstrate the designed speed controller. The control performance is compared between designed speed controller and traditional PID controller. The simulation results illustrate that the designed algorithm can satisfy the requirements of ATO system. The simulation performance of designed controller is better than PID controller. The feasibility and superiority of the ATO system with grey theory are proved.