A Survey of Control Algorithm for Automatic Train Operation

Chen, Xuanna; Ma, Wen-Loong; Xie, Guo; Hei, Xinhong; Wang, Feng; Tan, Siyu

doi:10.1109/iciea.2019.8833794

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…In addition, research based on train braking systems is moving towards a more in-depth direction, introducing more complex algorithms such as fuzzy PID control and neural network deep learning in various directions such as automatic train control and automatic speed regulation 6 . Therefore, it is necessary to establish a more accurate and applicable theoretical model of the braking system.0020This article proposes a method that combines fluid dynamics with software modeling, in order to improve the accuracy and reliability of train braking system modeling.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of freight train braking system based on AMESIM

Huang,

Chen,

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Air braking is the main form of braking for freight trains. With the rapid development of heavy-duty freight trains, the running speed of trains continues to increase, and the number of train formations and axle loads are constantly increasing. In order to cope with more complex brake environments, the braking system needs to further study the error in controlling train air pressure and methods to improve control accuracy. This article establishes a model of the entire vehicle air braking system based on AMESIM. Based on the different braking stages of freight trains as the theoretical basis, the train pipe constant pressure, air temperature, brake system pipe lengths, and air distribution valve parts parameters can be adjusted to obtain a model that is more in line with the actual situation. In addition, this paper confirms the accuracy of this model through mutual verification with brake tests.

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Section: Introductionmentioning

confidence: 99%

Modeling and simulation of freight train braking system based on AMESIM

Huang,

Chen,

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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“…PID control, which based on the characteristics formula of trains, employs traction, coasting, and braking modes to control the train. Despite its simple and easily implementable structure, this approach is associated with drawbacks such as poor disturbance resistance, frequent control switching, and shortcomings in terms of comfort and energy efficiency [1].…”

Section: Introductionmentioning

confidence: 99%

High-speed train automatic stopping control method based on deep reinforcement learning

Ma,

Shan,

Deng

et al. 2024

Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024)

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“…With the development of intelligent optimization algorithms, increasing attention is being paid to the research on automatic train operation (ATO) algorithms. The research focuses on train operation models [1] and control algorithms [2][3][4][5][6]. The key to the ATO algorithm of the maglev train is the speed curve track, which focuses on controlling the traction and braking force to track the target speed curve accurately [7,8].…”

Section: Introductionmentioning

confidence: 99%

Design of Nonlinear Active Disturbance Rejection Controller Based on the Adaptive Particle Swarm Optimization Algorithm for the Maglev Train Traction Control System

Fan

Wei

et al. 2023

Journal of Sensors

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This paper focuses on speed tracking control of the maglev train operation system. Given the complexity and instability of the maglev train operation system, traditional speed-tracking control algorithms demonstrate poor tracking accuracy and large tracking errors. The maglev train is easily affected by external interference, increasing train energy consumption and reducing passengers’ riding comfort. This study proposes a control algorithm called APSO-NLADRC to address the deficiencies of the automatic train operation control algorithms. The APSO-NLADRC is based on adaptive particle swarm optimization (APSO) algorithm parameter optimization nonlinear active disturbance rejection controller (NLADRC). The method of population comparison, linear update of learning factors, and adaptive updating of inertia weight values addresses the premature convergence phenomenon that occurs during the parameter optimization of the traditional particle swarm algorithm. The APSO algorithm solves the problem that the parameters of the NLADRC are difficult to adjust. Compared with PID, NLADRC, and NLADRC based on traditional particle swarm optimization algorithms, the proposed control algorithm has higher tracking accuracy and more robust anti-interference capability and provides better comfort.

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A Survey of Control Algorithm for Automatic Train Operation

Cited by 12 publications

References 11 publications

Modeling and simulation of freight train braking system based on AMESIM

Modeling and simulation of freight train braking system based on AMESIM

High-speed train automatic stopping control method based on deep reinforcement learning

Design of Nonlinear Active Disturbance Rejection Controller Based on the Adaptive Particle Swarm Optimization Algorithm for the Maglev Train Traction Control System

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