Active control strategy for the running attitude of high-speed train under strong crosswind condition

Li, Decang; Meng, Jianjun; Bai, Huiyu; Xu, Ruxun

doi:10.1080/00423114.2017.1403635

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Combining adaptive and dynamic matrix control algorithms, Li et al 42 proposed an active control strategy for a new-type intelligent giant magnetostrictive actuator to increase the safety level and ride comfort quality of HSTs under strong crosswind conditions. They further employed numerical simulation to verify the effectiveness of the proposed control strategy.…”

Section: Issues Directly Related To Train’s Vibration Controlmentioning

confidence: 99%

A combined review of vibration control strategies for high-speed trains and railway infrastructures: Challenges and solutions

Zhang

Kordestani

Shadabfar

2022

Journal of Low Frequency Noise, Vibration and Active Control

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People use trains as a means of transportation to travel to various nearby and distant destinations, resulting in an increasing amount of time spent inside such vehicles. Consequently, railway transportation sectors are focusing significantly more on enhancing passengers’ demand and level of satisfaction, including but not limited to high-speed travel, safety, and riding comfort. This paper provides a state-of-the-art review of available solutions for reducing undesired vibrations that have a substantial impact on improving the critical velocity limits of trains, maintaining the quality of riding comfort, and resolving safety concerns. In this regard, the solutions proposed for train vibration control are divided into two main categories: direct and indirect solutions. The direct solutions are those methods that are applied directly to trains’ bodies or bogies to attenuate the undesired vibrations, for example, improving the suspension/damper system, implementing active/semi-active control strategies, or applying various carriage optimization design modifications. Indirect solutions, on the other hand, are those that could indirectly affect trains’ vibrations, for example, controlling vibrations in railway bridge structures during train passages. Since the identification of vibration characteristics is assumed to be the first step in choosing proper solutions, particularly for active or semi-active control implementations, this review has examined the literature pertinent to modal identification of trains and railway infrastructures. Additionally, despite the installation of dampers between the bogie and suspension, the train’s equation of motion, and consequently, the vibration control of the bogie, has relied on the sky-hook model for decades. The fundamental problem for systems whose control strategies are based on the sky-hook concept is that the ‘sky’ does not actually exist. A solution to solve the sky-hook logic issue is using tuned rotatory inertia dampers and active rotatory inertia drivers which is addressed in this paper as well. Finally, it is concluded that these three solutions – employing a suspension/damper system in the bogie, an elastic connection for the train’s under-chassis-suspended equipment, and a ballastless railway – can practically mitigate vibration and noise in a train.

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Section: Issues Directly Related To Train’s Vibration Controlmentioning

confidence: 99%

A combined review of vibration control strategies for high-speed trains and railway infrastructures: Challenges and solutions

Zhang

Kordestani

Shadabfar

2022

Journal of Low Frequency Noise, Vibration and Active Control

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“…Heckmanm et al 24 taken an attractive control approach by using an electromagnetic attraction force between rail and axle box to improve wheel/rail vertical forces on the windward. Combined with the adaptive predictive and dynamic matrix control theory, Li et al 25 adopted a new-type intelligent giant magnetostrictive actuator to adjust the attitudes of head car of HST, and the running attitudes of HST were regulated effectively. Gao et al 26 investigated a semi-active suspension system of magneto-rheological fuzzy control strategy for ride comfort improvement subjected to fluctuating crosswind forces, however, only vertical motion was under control in their study.…”

Section: Introductionmentioning

confidence: 99%

An active suspension system for enhancing running safety of high-speed trains under strong crosswind

Zhang,

Ling,

Zhai

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part F:

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The running safety of trains subjected to strong crosswinds has become a major concern for the high-speed railways passing through complicated mountain areas. This paper reports an active secondary suspension system to improve the operation stability and running safety of high-speed trains under strong crosswind. The goal of the active suspension system is to regulate the lateral, yaw, and roll motion attitudes of high-speed train carbody, in which a controller is designed by combining with a disturbance observer and the sliding mode control method. To further verify the proposed active suspension strategy, a crosswind-vehicle-track coupled dynamics model is established, where the unsteady aerodynamic loads and random track irregularity excitations are considered. The results show that the proposed active suspension system has the efficient potential to regulate the carbody motion attitudes and enhance the anti-rolling performance of high-speed trains. In comparison to a quasi-static control strategy of active suspension, the use of the proposed active suspension system has led to a significant reduction in both wheel-load reduction ratios and derailment risks of high-speed trains.

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“…With the increase of the train speed, which makes the train more sensitive to the complex and changing environment, the complex and harsh environment (such as strong wind and sand, earthquake, mountain torrents, avalanches, landslides) seriously threatens the safety of train operation. ere are many derailments and overturning of train accidents caused by the harsh environment in the world rail transportation system [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High‐Speed Train

Zhang

et al. 2022

Discrete Dynamics in Nature and Society

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Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environment. Therefore, this paper proposes a novel multisource sensor data fusion method based on a three-level information fusion framework. Firstly, the feature of the same kind of sensor data is extracted by the Kalman Filter (KF) algorithm as the input of back propagation neural network (BPNN). Then input the sample site into the BPNN for training and recognition, the feature fusion of heterogeneous sensor data is carried out, the decision output of BPNN is obtained, the output results are normalized, and its output is used as the basic probability assignment of Dempster–Shafer (D-S) evidence theory and synthesis rules. Finally, the decision fusion of multisource data is realized by D-S evidence theory. The simulation results show that compared with the traditional single fusion algorithm, the algorithm improves the accuracy of the prediction of high-speed train operation environment and reduces the MAPE from 13.82% to 7.455%, and the RMSE from 0.77 to 0.69, and meanwhile, increases the R2 from 0.87 to 0.97.

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Active control strategy for the running attitude of high-speed train under strong crosswind condition

Cited by 8 publications

References 11 publications

A combined review of vibration control strategies for high-speed trains and railway infrastructures: Challenges and solutions

A combined review of vibration control strategies for high-speed trains and railway infrastructures: Challenges and solutions

An active suspension system for enhancing running safety of high-speed trains under strong crosswind

A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High‐Speed Train

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