Iterative Learning Operation Control of High-Speed Trains With Adhesion Dynamics

Abstract

This article proposes a new spatial iterative learning control (ILC) algorithm that can track the desired trajectory for operation control of high-speed train (HST) in the presence of time-varying adhesion dynamics between wheel and track. Although the adhesion dynamics will affect the overall performance and might lead to an unsafe scenario, not much work has been done to handle them in the field of operation control of HST. As the operation process of HST is repetitive in spatial domain, the conventional ILC algorithms, in which finite time domain is always considered, cannot be directly applied to such kind of system. In order to address spatial learning, the train’s operation dynamics, including the adhesion dynamics, are first developed. By revealing the link between the temporal gradient and the spatial gradient, the operation dynamics can be converted from the time domain into the spatial domain, making the ILC design feasible. A novel ILC algorithm is then proposed to ensure the convergence with the help of a new composite energy function (CEF). It is highlighted that in this CEF analysis, dynamic systems are not required to be globally Lipschitz, which is one of the standing assumptions needed in the convergence analysis of ILC. The effectiveness of the proposed spatial ILC algorithm is demonstrated from a simulation example.