Open Access

ARTICLE

Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization

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1 School of Electronic and Information Engineering, Beijing Jiaotong University

Computer Modeling in Engineering & Sciences 2013, 90(6), 415-437. https://doi.org/10.3970/cmes.2013.090.415

Abstract

High-speed train transportation is organized in a way of globally centralized planning and locally autonomous adjustment with the real-time known positions, speeds and other state information of trains. The hierarchical integration architecture composed of top, middle and bottom levels is proposed based on model predictive control (MPC) for the real-time scheduling and control. The middle-level trajectory configuration and tractive force setpoints play a critical role in fulfilling the top-level scheduling commands and guaranteeing the controllability of bottomlevel train operations. In the middle-level MPC-based train operation planning, the continuous cellular automaton model of train movements is proposed to dynamically configure the train operation positions and speeds at appointed time, which synthetically considers the scheduling strategies from the top layer, and the tempospatial constraints and operation statuses at the bottom level. The macroscopic dynamic model of a train predicts the trajectories under the candidate control sequences. Through Levenberg-Marquardt optimization, the feasible tractive forces and updated trajectories are attained under the power constraints of electric machines. Numerical results have demonstrated the effectiveness of proposed control planning technique. This paper reveals the utilities of different-level models of train movements for the accomplishment of railway network operation optimization and the guaranty of individual train operation safety. It also provides a solution to automatic trajectory configuration in the automatic train protection (ATP) and operation (ATO) systems.

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Keywords

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