Abstract
Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method.
Highlights
With the ever-accelerating process of urbanization, transportation infrastructure and traffic management can not meet the growing traffic demand
The aim of this paper was to investigate the energy-saving train regulation problem for a single metro line based on distributed model predictive control (DMPC), by assuming each train is self-organized and capable to communicate with other trains
We firstly present a distributed control framework for energy-saving train regulation in metro open lines, and propose a DMPC algorithm for energy-saving train regulation of metro open lines with operational constraints taken into account
Summary
With the ever-accelerating process of urbanization, transportation infrastructure and traffic management can not meet the growing traffic demand. Urban metro transportation is considered an ideal solution to ease the traffic pressure in large and populous cities due to its huge capacity, safety, and punctuality [1]. Metro lines are known to be inhrerently unstable since unavoidable disturbances will bring deviations from the scheduled timetable and any deviation will be amplified with time [2,3]. To prevent such instability, online train regulation is very necessary for scheduled timetable recovery by dynamically manipulating the running time and staying time of each train.
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