CNN-based adaptive tracking control for high speed train with asymmetric input saturation

Abstract

This paper studies the adaptive position and velocity tracking control for high speed train with asymmetric nonlinear input saturation. Adaptive tracking control for high speed train encounters great challenges when the train dynamics containing nonlinearities, asymmetric input saturation and unknown external disturbances. An adaptive tracking control method is developed by means of sliding mode technique combining with adaptive Chebyshev neural network (CNN) technique. Through rigorous analysis, it is concluded that the design control method can ensure uniformly ultimate bounded convergence of train trajectory to the reference profile. In particular, the assumption with zero initialization condition for train velocity and reference velocity is removed. Numerical simulation results further verify the effectiveness of the proposed method.