Spatial Adaptive Iterative Learning Control Based High-Speed Train Operation Tracking under External Disturbance

In order to solve the trajectory tracking problem of high-speed train automatic operation systems under external random disturbance, the temporal train aerodynamic model is transformed into a spatial train model by using the spatial differentiator. Firstly, this paper proposes a spatial adaptive learning control (SAILC) algorithm based on the recursive least square (RLS) algorithm to identify parameters and learn the repetitive information of the system, so as to drive the train to automatically track the desired speed trajectory. Secondly, the spatial composite energy function (SCEF) based on Lyapunov is established, and its differential negative definiteness and boundedness are proved. Finally, the proposed SAILC algorithm is compared with an iterative learning control algorithm. The results show that the proposed algorithm has better performance on convergence speed and anti-interference capability.