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Research on speed control of high-speed train based on multi-point model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The traditional train speed control research regards the train as a particle, ignoring the length of the train and the interaction force between carriages. Although this method is simple, the control error is large for high-speed trains with the characteristics of power dispersion. Moreover, in the control process, if the length of the train is not considered, when the train passes the slope point or the curvature point, the speed will jump due to the change of the line, causing a large control error and reducing comfort. In order to improve the accuracy of high-speed train speed control and solve the problem of speed jump when the train runs through variable slope and curvature, the paper takes CRH3 EMU data as an example to establish the corresponding multi-point train dynamics model. In the control method, the speed control of high-speed train needs to meet the fast requirement. Comparing the merits and demerits of classical PID control, fuzzy control and fuzzy adaptive PID control in tracking the ideal running curve of high-speed train, this paper chooses the fuzzy adaptive PID control with fast response. Considering that predictive control can predict future output, a predictive fuzzy adaptive PID controller is designed, which is suitable for high-speed train model based on multi-point. The simulation results show that the multi-point model of the high-speed train can solve the speed jump problem of the train when passing through the special lines, and the predictive fuzzy adaptive PID controller can control the speed of the train with multi-point model, so that the train can run at the desired speed, meeting the requirements of fast response and high control accuracy.
Rocznik
Strony
35--46
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
autor
  • Lanzhou Jiaotong University, School of Automation and Electrical Engineering, Lanzhou, P.R. China
  • Lanzhou Jiaotong University, School of Automation and Electrical Engineering, Lanzhou, P.R. China
autor
  • Lanzhou Jiaotong University, Automatic Control Research Institute, Lanzhou, P.R. China
Bibliografia
  • [1] ANSARI, M., ESMAILZADEH, E., & YOUNESIAN, D., 2009. Longitudinal dynamics of freight trains. International Journal of Heavy Vehicle Systems, 16(1), 102-131.
  • [2] ASTOLFI, A., & MENINI, L., 2002. Input/ out-put decoupling problems for high speed trains. American Control Conference. Proceedings, Danvers, MA, USA, 549-554
  • [3] CHEN, X.Q., MA, Y.J.,& HOU, T., 2014. Study on Speed Control of High-Speed Train Based on Predictive Fuzzy PID Control. Journal of System Simulation, 26(1), 191-196.
  • [4] CHOU, M., & XIA, X., 2007. Optimal cruis control of heavy-haul trains equipped with electronically controlled pneumatic brake systems. Control Engineering Practice, 15(5), 511-519.
  • [5] HOU, T., 2015. Speed Control Study of Multi-mode Intelligent Control Based on Multi-information Fusion and Filter on High-speed Train. Lanzhou: lanzhou Jiaotong University.
  • [6] LI, Z.Q., DING, J.Y., YANG, H., et al, 2018. Generalized Predictive Control Tuning for High-speed Train Based on Controller Matching Method. Journal of The China Railway Society, 40(9), 82-87.
  • [7] LIN, C.J., TSAI, S.H., CHEN, C.L., et al, 2010. Extended sliding-mode controller for high speed train. Proceedings of the 2010 International Conference on System Science and Engineering, Piscataway, NJ, USA: IEEE, 475-480.
  • [8] LIU, H., QIAN, C.Y., & SHI, Z.D., 2017. ATO System Control Algorithm Based on Fuzzy Adaptive PID. Urban Mass Transit, (3), 40-43.
  • [9] LIU, H.W., ZHAO,H.D.,&JIA, L.M., 2000. A Study on the Control Algorithm for Automatic Train Operation. China Railway Science, 21(4), 38-43.
  • [10] LU, X.J., WANG, X.J., DONG, H.Y., et al, 2016.Research on Sliding Mode Predictive Control of Energy-saving Operation of High-speed Train. Control Engineering of China, 23(3), 289-293.
  • [11] LUO, H.Y., &XU, H.Z.,2013. Study on Model Reference Adaptive Control of ATO Systems. Journal of the China Railway Society, 35(7), 68-73.
  • [12] MA, Y.J., CHEN, X.Q., & HOU, T., 2013. Study on the Speed Control of High-speed Train Based on Fuzzy Predictive Control. Computer Measurement & Control, 21(1), 96-99.
  • [13] MIKULSKI, J., & GORZELAK, K., 2017. Conception of modernization of a line section example in the context of a fast railway connection. Archives of Transport, 44(4), 47-54.
  • [14] REN, L.J., FAN, D.W.,& YANG, J.X., 2017. Research on ATO Algorithm Based on Fuzzy PID Control with Optimized GA. Railway Standard Design, 61(2),127-130.
  • [15] SONG,Q., SONG, Y.D., TANG, T., et al, 2011. Computationally inexpensive tracking control of high-speed trains with traction/braking saturation. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1116-1125.
  • [16] TANG, T., & HUANG, L.J., 2003. A survey of control algorithm for automatic train operation. Journal of the China Railway Society, 25(5), 98-102.
  • [17] WANG, L.S., 2016. Predictive Control for Automatic Operation of High-Speed Trains Based on Multi-point Model. Beijing: beijing Jiaotong University.
  • [18] WIECZOREK, S., PALKA, K.,& GRABOWSKA-BUGNA, B., 2018. A model of strategic safety management in railway transport based on Jastrzebska Railway Company Ltd. Scientific Journal of Silesian University of Technology. Series Transport, 98, 201-210.
  • [19] WU, Z.F., YANG, E.Y., & DING, W.C., 2017. Design of large-scale streamlined head cars of high-speed trains and aerodynamic drag calculation. Archives of Transpoint, 44(4), 90-97.
  • [20] XIE, G., ZHANG, D., HEI, X.H., et al, 2017. Online identification method of time-varying parameters for longitudinal dynamics model of high-speed train. Journal of Traffic and Transportation Engineering, 17(1), 71-81.
  • [21] YANG, C.D., & SUN, Y.P., 2001. Mixed H2/H cruise controller design for high speed train. International Journal of Control, 74(9), 905-920.
  • [22] YANG, H., ZHANG, K.P., WANG, X., et al, 2011. Generalized Multiple-model Predictive Control Method of High-speed Train. Journal of the China Railway Society, 33(8), 80-87.
  • [23] ZHANG , L., & ZHUAN, X., 2013. Braking-penalized receding horizon control of heavy-haul trains. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1620-1628.
  • [24] ZHANG, L., & ZHUAN, X., 2015. Development of an optimal operation approach in the MPC framework for heavy-haul trains. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1391-1400.
  • [25] ZHANG, L., & ZHUAN, X., 2014. Optimal operation of heavy-haul trains equipped with electronically controlled pneumatic brake systems using model predictive control methodology. IEEE Transactions on Control Systems Technology, 22(1), 13-22.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8e68c2f0-af1e-4b8c-ac7e-752b7fad2a28
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