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Research on speed tracking control algorithm of the high-speed train based on equivalent sliding mode and RBF neural network

Aiming at the problem of accurate tracking control of high-speed train autopilot system under complex operation environment, a neural network sliding mode equivalent controller is designed based on the single point dynamic model of the train. Using the real-time state deviation in the process of train operation, an equivalent controller based on sliding mode theory is designed. The neural network switching controller is designed to suppress the inherent chattering of sliding mode control and compensate for the influence of external nonlinear factors in the process of train operation. The results show that the controller can track the reference trajectory accurately when considering the additional resistance and external disturbance.