China's railway train speed, density and weight in developing

Bo, Jiang; Tian, Changhai; Deng, Jiehang; Zhu, Zhimin

doi:10.1108/rs-04-2022-0009

Cited by 6 publications

(4 citation statements)

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“…Heavy haul trains have a large transportation capacity, high efficiency, and low transportation costs; thus, they have received widespread attention from countries worldwide. To control speed when heavy haul trains operate on long downhill sections, the braking system must increase cyclic air braking [1]. The existing strategy used for air braking mainly relies on the conductor's experience, which is insufficient for meeting the safety and efficiency requirements for heavy haul train operation [2].…”

Section: Introductionmentioning

confidence: 99%

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Zhang,

Zhou,

et al. 2024

Information

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Cyclic air braking is a key factor affecting the safe operation of trains on long downhill sections. However, a train’s cycle braking strategy is constrained by multiple factors such as driving environment, speed, and air-refilling time. A Q-learning algorithm-based cyclic braking strategy for a heavy haul train on long downhill sections is proposed to address this challenge. First, the operating environment of a heavy haul train on long downhill sections is designed, considering various constraint parameters, such as the characteristics of special operating routes, allowable operating speeds, and train tube air-refilling time. Second, the operating status and braking operation of a heavy haul train on long downhill sections are discretized in order to establish a Q-table based on state–action pairs. The training of algorithm performance is achieved by continuously updating Q-tables. Finally, taking the heavy haul train formation as the study object, actual line data from the Shuozhou–Huanghua Railway are used for experimental simulation, and different hyperparameters and entry speed conditions are considered. The results show that the safe and stable cyclic braking of a heavy haul train on long downhill sections is achieved. The effectiveness of the Q-learning control strategy is verified.

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Section: Introductionmentioning

confidence: 99%

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Zhang,

Zhou,

et al. 2024

Information

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“…With the rapid development of railway communication technology, automatic driving technology and train positioning system, conditions are created for multi-train cooperative control. During the tracking operation of high-speed train, real-time and high-quality information interaction is realized between trains through Radio Block Center (RBC) and other ground equipments, and the cooperative control between trains is realized through intelligent control algorithm based on automatic driving technology, so as to achieve smaller tracking interval and accurate tracking of the expected speed curve, thus improving traffic efficiency, safety and comfort (Tian, 2020). However, today's control algorithms ignore the influence of interference on the control system, so improving the control algorithm and enhancing the immunity of the system is of far-reaching significance to the research of multi-train collaborative control.…”

Section: Introductionmentioning

confidence: 99%

High-speed train cooperative control based on fractional-order sliding mode adaptive algorithm

Lin

Liang

2023

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PurposeThis study aims to propose an adaptive fractional-order sliding mode controller to solve the problem of train speed tracking control and position interval control under disturbance environment in moving block system, so as to improve the tracking efficiency and collision avoidance performance.Design/methodology/approachThe mathematical model of information interaction between trains is established based on algebraic graph theory, so that the train can obtain the state information of adjacent trains, and then realize the distributed cooperative control of each train. In the controller design, the sliding mode control and fractional calculus are combined to avoid the discontinuous switching phenomenon, so as to suppress the chattering of sliding mode control, and a parameter adaptive law is constructed to approximate the time-varying operating resistance coefficient.FindingsThe simulation results show that compared with proportional integral derivative (PID) control and ordinary sliding mode control, the control accuracy of the proposed algorithm in terms of speed is, respectively, improved by 25% and 75%. The error frequency and fluctuation range of the proposed algorithm are reduced in the position error control, the error value tends to 0, and the operation trend tends to be consistent. Therefore, the control method can improve the control accuracy of the system and prove that it has strong immunity.Originality/valueThe algorithm can reduce the influence of external interference in the actual operating environment, realize efficient and stable tracking of trains, and ensure the safety of train control.

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“…In order to ensure the safety of bridge structure and vehicle operation, it is necessary to study the vehicle bridge coupling vibration of highway and railway bridges under the action of vehicle train overlap. Reference [7] established a vehicle model with 7 degrees of freedom. The vehicle model only considers vertical vibration, and the wheelset moment is closely attached to the bridge deck; a light rail vehicle model with 15 degrees of freedom is established, and the wheelset moment of the light rail is closely related to the track beam.…”

Section: Introductionmentioning

confidence: 99%

Simulation Analysis of Coupled Structural Vibration of Highway Railway Combined Bridge Induced by Overlapping Action of Vehicle and Train

Peng¹,

Xin²,

et al. 2022

Geofluids

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In order to study the dynamic response of bridge coupling caused by the overlapping of vehicle and railway dynamic loads, by using the time-domain samples of pavement irregularity compiled by MATLAB software and the secondary development function of ANSYS software, the vehicle, bridge, and train models are established in ANSYS software at the same time, and the train supported beam bridge coupling program is compiled with MATLAB software. The model considers the influence of initial conditions and vehicle speed on the system before entering the bridge, the interaction between the coupled large-scale highway and railway bridge system, and the influence of different adverse factors on the system. The influence of a vehicle or train on the bridge structure when the vehicle or train passes through the continuous steel truss bridge under different factors is calculated, respectively, and the displacement and acceleration limits of the bridge are evaluated in combination with the corresponding specifications.

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China's railway train speed, density and weight in developing

Cited by 6 publications

References 0 publications

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

High-speed train cooperative control based on fractional-order sliding mode adaptive algorithm

Simulation Analysis of Coupled Structural Vibration of Highway Railway Combined Bridge Induced by Overlapping Action of Vehicle and Train

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