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Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information

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Abstract

Optimal trajectory planning of high-speed trains (HSTs) aims to obtain such speed curves that guarantee safety, punctuality, comfort and energy-saving of the train. In this paper, a new shrinking horizon model predictive control (MPC) algorithm is proposed to plan the optimal trajectories of HSTs using real-time traffic information. The nonlinear longitudinal dynamics of HSTs are used to predict the future behaviors of the train and describe variable slopes and variable speed limitations based on real-time traffic information. Then optimal trajectory planning of HSTs is formulated as the shrinking horizon optimal control problem with the consideration of safety, punctuality, comfort and energy consumption. According to the real-time position and running time of the train, the shrinking horizon is updated to ensure the recursive feasibility of the optimization problem. The optimal speed curve of the train is computed by online solving the optimization problem with the Radau Pseudo-spectral method (RPM). Simulation results demonstrate that the proposed method can satisfy the requirements of energy efficiency and punctuality of the train.

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou Zhejiang, 310023, China

    Defeng He, Long Zhou & Zhe Sun

Authors
  1. Defeng He
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  2. Long Zhou
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  3. Zhe Sun
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Corresponding author

Correspondence to Defeng He.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 61773345) and the Zhejiang Provincial Natural Science Foundation (No. LR17F030004).

Defeng HE received the B.Sc. degree from Central South University, Changsha, China, in 2001 and the Ph.D. degree from University of Science and Technology of China, Hefei, China, in 2008. From 2008 to 2010, he was a lecturer at Zhejiang University of Technology, Hangzhou, China, from 2010 to 2015 he was an associate professor, and since 2015 he has be a professor at the same university. From December 2014 to December 2015, he was a visiting scholar at University of Michigan, Ann Arbor, U.S.A. His research interests include model predictive control and its applications to smart interconnected systems.

Long ZHOU received the B.Sc. degree in Electrical Engineering from Luoyang Institute of Technology, Louyang, China, in 2013, and the M.Sc. degree in Control Engineering from Zhejiang University of Technology, Hangzhou, China, in 2017, where he is currently pursuing the M.Sc. degree in Control Engineering.

Zhe SUN received the B.Eng. degree in Transportation Engineering from Nanjing Agricultural University, Nanjing, China, in 2011, and the Ph.D. degree from Swinburne University of Technology, Melbourne, Australia, in 2017. From 2017 to 2019, he was a postdoctoral research associate in Swinburne University of Technology, Melbourne, Australia. Currently, he is serving as an associate professor in Zhejiang University of Technology, Hangzhou, China. His research interests include sliding control, vehicle dynamics and control.

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He, D., Zhou, L. & Sun, Z. Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information. Control Theory Technol. 18, 204–216 (2020). https://doi.org/10.1007/s11768-020-0001-x

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  • DOI: https://doi.org/10.1007/s11768-020-0001-x

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