High-Order Internal Model-Based Data-Driven Iterative Learning Control of High-Speed Railways Subject to Faded Channels

This study investigates the high-order internal model (HOIM) based data-driven iterative learning control of HSRs subject to faded channels. Firstly, the nonlinear train dynamics are converted into an input/output data-based model by using a linearization approach. Then, the HOIM of the desired speed trajectories is introduced and the fading channel is used to model the unreliable transmission network. Next, the model free adaptive iterative learning control (MFAILC) strategy is implemented based on the train input and faded output information, and the theoretical convergence analysis of the speed error is carried out. Eventually, the validity of the MFAILC scheme is checked in simulation by applying the CRH-380 HSRs on a StarSim hardware-in-loop semi-physical platform.