An Intelligent Cruise Controller for High-Speed Train Operation Based on Fuzzy Neural Network Theory

Abstract

An intelligent control strategy is proposed in this paper, which is applied to the high-speed train ATO (Automatic Train Operation) system in the cruise condition. The dynamics of a high-speed train is discussed based on a typical single-point-mass model and the force analysis in cruise state is studied. A fuzzy neural network control algorithm is incorporated into the ATO system aiming at improving the velocity and position tracking performance in the cruise operation of high-speed train. This control scheme adjusts the parameters of membership functions on-line and does not rely on the precise system parameters such as resistance coefficients which are very difficult to measure in practice. The numerical simulation verifies the effectiveness of this fuzzy neural network algorithm.