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Urban Rail Transit Automatic Train Operation System Online Algorithm Research

Automatic Train Operation(ATO) used in current urban rail systems combines with two levers of control. The upper level of control is a recommended curve optimized offline with some operation performance indicators. The lower level of control tracks the recommended curve with specified strategies. While the running environment changes, the operation performance indicators can not be satisfied any more. Moreover, for multi-level of operation, multi recommended curves generated off line consume a large storage of the ATO systems. Even, time delay will be caused by these pre-setting curves in some situations. This paper proposed an online ATO algorithm based on neural network predictive control with energy optimization, which is adapting to the variety of running environment. The neural networks are used as the predictive model of the nonlinear system. Two types of neural networks, Error Back Propagation (BP) Neural Network and Radial Basis Function (RBF) Neural Network, are studied and compared in this paper. As the energy-saving is considered in the performance indicators, as well as the accurate parking and punctuality, the Modified Particle Swarm Optimization(MPSO) is used as the rolling optimization algorithm to reduce the computation. Simulation carried with field data shows a good performance and practicality of this algorithm.