@inproceedings{d0400546d37a417884be7af856b69b4a,
title = "A Linear Quadratic Gaussian Optimal Control Approach for Train Platoon Formation",
abstract = "Multi-train intelligent formation control is a promising way to improve the traffic capacity of the railway system and cope with the increasing travel demand. This paper presents an optimal control method based on linear quadratic Gaussian (LQG) control, which makes multiple trains form a stable train formation. We formulate the issue of train formation control as an optimal trajectory tracking problem to minimize the influence of system disturbance and noise. A reference trajectory generator is designed by model predictive control (MPC) and a trajectory tracker based on LQG control is proposed, which combines linear quadratic regulator (LQR) and Kalman filter to improve the robustness of the formation system. The optimal control given by the proposed model has been verified theoretically and algorithmically. The proposed model shows good control effects and stable train convoy is realized during the formation process.",
keywords = "LQG control, MPC, reference trajectory, train formation, trajectory tracking",
author = "Daxin Tian and Jiawei Li and Jianshan Zhou and Xuting Duan and Zhengguo Sheng and Dezong Zhao and Wei Hao and Kejun Long",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Unmanned Systems, ICUS 2021 ; Conference date: 15-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1109/ICUS52573.2021.9641350",
language = "英语",
series = "Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "116--121",
booktitle = "Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021",
address = "美国",
}