Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking With Overspeed Protection

Abstract

This work embodies the overspeed protection and safe headway control into an iterative learning control (ILC) based train trajectory tracking algorithm to satisfy the high safety requirement of high-speed railways. First, a D-type ILC scheme with overspeed protection is proposed. Then, a corresponding coordinated ILC scheme with multiple trains is studied to keep the safe headway. Finally, the control scheme under traction/braking force constraint is also considered for this proposed ILC-based train trajectory tracking strategy. Rigorous theoretical analysis has shown that the proposed control schemes can guarantee the asymptotic convergence of train speed and position to its desired profiles without requirement of the physical model aside from some mild assumptions on the system. Effectiveness is further evaluated through simulations.Note to Practitioners-This work was motivated by the outstanding repetitive operation pattern and the high safety requirements of high-speed railways. By utilizing the repetitive operation feature of a train, iterative learning control strategy is applied to improve control performance by learning repetitive information. To avoid excessive speed, which is regarded as a severe unsafe factor for the high-speed train, an overspeed penalty is added as a state constraint. To keep safe headway between adjacent trains, the adjacent trains' information is used to construct the coordinated iterative learning control strategy. To further enhance the applicability, the corresponding control scheme under traction/braking force constraint is also presented. In addition, this approach is a data driven model-free control method. The above method can also be applied to other repetitive operation systems with state constraint, input constraint, or requirement of coordination operation. Although the feasibility and effectiveness of this ILC-based train control method has been proved through the rigorous theoretical analyses without requirement of the train dynamics model as long as some mild reasonable assumptions are satisfied, and the simulation research as well, the field experiments have not been carried out yet. In our future research, we will focus on this issue and address some other practical concerns, such as the operation-depended uncertainties and disturbances in train operations.

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“…where M is a Markov matrix of rank N and whose terms are Markov parameters of the plant as cited in [33] and…”

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