Iterative Learning Identification Method for the Train Motion Model

This paper proposes a practical method to identify the parameters in the train motion model. Focusing on the repeatability of the train motion system, the new method develops an iterative learning identifier to estimate the parameters through repetitive trials. With rigorous analysis, it is shown that each parameter can effectively converge to its actual point after enough iteration. At last, the numerical simulation demonstrates the validity of the proposed method.