Iterative learning control based automatic train operation with iteration-varying parameter

An iterative learning control (ILC) based on Automatic Train Operation (ATO) is proposed to address train speed tracking problem under iteration-varying operation condition, that is, the air resistance coefficients of the train at any two consecutive iterations are completely different. Using the data measured by the sensors, repetitive requirement of traditional ILC is partially relaxed in the proposed method. The effectiveness of the proposed method is verified by theoretical analysis and numerical simulation.