Robust point‐to‐point iterative learning control for high speed trains with model uncertainty and wind gust

Abstract In this paper, a point‐to‐point iterative learning control strategy for a cascaded multibody high‐speed train (HST) system with model uncertainty and external disturbance is designed to address a specified given desired points tracking problem. The proposed method, which only used desired point information rather than whole trajectory information, is used to improve the multiple‐point tracking accuracy by enjoying the repetitiveness of an HST. A norm‐optimal method is employed in the ILC operating framework to analyze the HST model with parametric uncertainties and external disturbances. Both a rigorous mathematical analysis and detailed simulation results confirm the correctness and effectiveness of the proposed method.