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Research on speed control of high speed trains based on hybrid modeling

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Języki publikacji
EN
Abstrakty
EN
With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the combination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual operation data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was developed based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conducted. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and operation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and provide a reference for automatic driving and intelligent driving of high-speed trains.
Rocznik
Strony
77--82
Opis fizyczny
Bibliogr. 22 poz., rys., tab., wykr., wzory
Twórcy
autor
  • Lanzhou Jiaotong University, School of Automation and Electrical Engineering, Lanzhou, China
autor
  • Lanzhou Jiaotong University, School of Automation and Electrical Engineering, Lanzhou, China
autor
  • Lanzhou Jiaotong University, School of Automation and Electrical Engineering, Lanzhou, China
  • Lanzhou Jiaotong University, School of Automation and Electrical Engineering, Lanzhou, China
Bibliografia
  • [1] Karolak, J. (2021). Interface and connection model in the railway traffic control system. Archives of Transport, 58(2), 137-147.
  • [2] Tan, C., Li, Y. Q. (2022). Adaptive braking control for high-speed trains with input time delays. Journal of Railway Science and Engineering, 19(04), 1071-1080.
  • [3] Lian, W. B., Liu, B. H., Li, W. W., et al. (2020). Automatic operation speed control of high speed train based on ADRC. Journal of the China Railway Society, 42(01), 76-81.
  • [4] Zhang, W. J., Cao, B.W., Liu, Y. F., et al. (2022). Operation control method for medium speed maglev trains based on fractional order sliding mode adaptive neural network. China Railway Science, 43(2), 152-160.
  • [5] Hou, T, Guo, Y. Y., Niu, H. X. (2019). Re search on speed control of high-speed train based on multi-point model. Archives of Trans port, 50(02), 35-46.
  • [6] Jia, C., Xu, H. Z., Wang, L. S. (2020). Nonlinear model predictive control for automatic train operation based on multi-point model. Journal of Jilin University(Engineering and Techno logy Edition), 50(05), 1913-1922.
  • [7] Mo, X. T., Wang, X. Q., Liang, X. R., et al. (2021). Speed tracking control for high-speed trains with Fuzzy RBF neural network. Modern Computer, 27(26), 1-7+14.
  • [8] Jiang, B., Chen, H. T., Yi, H., et al. (2020). Data-driven fault diagnosis for dynamic traction systems in high-speed trains. Scientia Sinica(Informationis), 50(04), 496-510.
  • [9] Fu, C. X., Zhao, T. (2022). Fault identification based on data-driven method for traction con trol systems in high-speedtrain. Microcomputer Application, 38 (10), 138-141.
  • [10] Wang, H., Liu, G. F., Hou, Z. S. (2022). Data driven model-free adaptive fault tolerant control for high-speed trains. Control and Decision, 37(5), 1127-1136.
  • [11] Zhang, M. X., Liu, H. C., Wang, M., et al. (2021). Intelligence hybrid modeling method and applications in chemical process. Chemical Industry and Engineering Progress, 40(4), 1765-1776.
  • [12] Anifowose, F. A., Labadin, J., Abdulraheem, A. (2017). Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead. Journal of Petroleum Exploration and Production Technology, 7(1), 251-263.
  • [13] Kim, J., Chen, M., Han, J. J., et al. (2021). The development of leak detection model in subsea gas pipeline using machine learning. Journal of Natural Gas Science and Engineering, 94, 104134. [14] Hou, T. (2015). Speed control study of multimode intelligent control based on multi-information fusion and filter on high-speed train. Lanzhou: Lanzhou Jiaotong University.
  • [15] Ding, P. (2021). A multiple point-mass podel based high-speed train adaptive speed tracking control scheme. Nanchang: East China Jiaotong University.
  • [16] Liu, G., Yuan, Z. Y., Chen, L., et al. (2021). Preliminary study on application of hybrid modeling method in oil and gas pipeline networks. Oil & Gas Storage and Transportation, 40(09), 980-990.
  • [17] Li, J. X., Zhou, D. J., Xiao, W., et al. (2019). Hybrid Modeling of Gas Turbine based on Neural Network. Journal of Engineering for Thermal Energy and Power, 34(12), 33-39.
  • [18] Garca-matos, J. A., Sanz-bobi, M. A., Sola, A. (2013). Hybrid Model-based Fault Detection and Diagnosis for the Axial Flow Compressor of a Combined-cycle Power Plant. Journal of Engineering for Gas Turbines and Power, 135(5), 054501.
  • [19] Ferrer, S., Mezquita, A., Aguilella, V. M., et al. (2019). Beyond the energy balance: Exergy analysis of an industrial roller kiln fring porcelain tiles. Applied Thermal Engineering, (150), 1002-1015.
  • [20] Liang, Y. Y. (2021). Research on predictive of firing zone temperature in roller kiln based on mechanism and data hybrid driven. Guangzhou: Guangdong University of Technology.
  • [21] Hou, T., Guo, Y.Y., Chen, Y., Yang, H.K. (2020). Study on speed control of high-speed train based on multi-point model. Journal of Railway Science and Engineering, 17(02), 314-325.
  • [22] Yang, H. K., Hou, T., Chen Y. (2022). Research on optimal control of high speed trains based on predictive Fuzzy PID control algorithm. Railway Transport and Economy, 44(8),130-136.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-002b3484-c480-4137-b156-9390b099dd1d
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