Spatial Adaptive Iterative Learning Control for Automatic Driving of High Speed Train

This paper develops spatial adaptive iterative learning control (SAILC) method for the Automatic Train Operation (ATO) of high speed train in order to drive the train speed to follow the desired displacement-speed trajectory. By utilizing spatial state differentiator, the temporal nonlinear train control system is transformed to a spatial nonlinear train control system. The system uncertainties which are space dependent are learned by a spatial parametric updating law. By adopting the spatial composite energy function, the convergence of the SAILC for train control system is obtained. Furthermore, the convergence of the speed error in spatial domain under iteration-varying initial speed of the proposed SAILC is made. Numerical results of the train operation are given to verify the effectiveness of the proposed SAILC method.