Trajectory Tracking by an Adaptive Controller for High-Speed Train Based on Neural Network and Sliding Mode Control

This paper proposes an adaptive sliding mode controller (ASMC) for trajectory tracking of high-speed trains (HST) with uncertainties. The ASMC incorporates a radial basis function neural network (RBFNN) to approximate the model uncertainties and uses a sigmoid function to reduce chattering. The Lyapunov function is employed to prove the stability of the controller. Numerical simulations using the data of China Railway High-Speed (CRH) 380 train demonstrate the effectiveness of the controller in ensuring the train follows the tracking target under different prior knowledge conditions.