Iterative learning control for train trajectory tracking under speed constrains with iteration-varying parameter

Abstract

An iterative learning control based on Automatic Train Operation is proposed for repetitively running train to deal with trajectory tracking problem under iteration-varying operation condition and certain speed constrains. Iteration-varying operation condition considered in this paper focuses on the air resistance coefficient of the train, which may be completely different at any two consecutive operation processes due to different weather conditions. To eliminate the influence of the resistance coefficient caused by the different weather conditions, data measured by the temperature sensors is used. In order to meet the speed constrains, the Barrier Composite Energy Function scheme is used to guarantee that the speed constraints are not violated. The effectiveness of the proposed method is verified by theoretical analysis and numerical simulation.