Volume 23 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(2): 264-272
Next Previous
ZHANG Wen-jing, RUAN Yu-xin, GAO Ya-ping, CHEN Yu-feng, YUE Qiang, XU Hong-ze. Sliding mode periodic adaptive learning control method for medium-speed maglev trains[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 264-272. doi: 10.19818/j.cnki.1671-1637.2023.02.019
Citation: ZHANG Wen-jing, RUAN Yu-xin, GAO Ya-ping, CHEN Yu-feng, YUE Qiang, XU Hong-ze. Sliding mode periodic adaptive learning control method for medium-speed maglev trains[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 264-272. doi: 10.19818/j.cnki.1671-1637.2023.02.019
shu

Sliding mode periodic adaptive learning control method for medium-speed maglev trains

doi: 10.19818/j.cnki.1671-1637.2023.02.019

  • School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Key Research and Development Program of China 2016YFB1200601

Aeronautical Science Foundation of China 2019010M5001

More Information
  • Author Bio:

    ZHANG Wen-jing(1976-), male, associate professor, PhD, zhangwj@bjtu.edu.cn.

  • Received Date: 2022-12-07
    Available Online: 2023-05-09
  • Publish Date: 2023-04-25
    Abstract
  • In order to improve the operation control performance of the medium-speed maglev train, the periodic characteristic of the train running along a fixed line was considered, and an operation control algorithm based on the sliding mode periodic adaptive learning control (SMPALC) method for the train was proposed. The corresponding sliding mode periodic adaptive controller was composed of the equivalent proportional-integral-differential (PID) and speed feedforward control part, the magnetic resistance and air resistance compensation part, and the ramp resistance sliding mode periodic adaptive compensation part. The operation controller parameters were tuned through the particle swarm optimization (PSO) algorithm. The sliding mode periodic adaptive controller was used to learn the train operation information in the previous period, the ramp resistance during train operation in real time was estimated and compensated, and the influence of ramp resistance on the train operation performance was eliminated. The semi-physical simulation test line of a medium-speed maglev train with a total route length of 5 076 m was numerically simulated, and the designed sliding mode periodic adaptive controller was compared with the PID controller through the simulation. Simulation results show that the maximum position tracking errors under the sliding mode periodic adaptive controller and PID controller are 0.004 and 0.007 m, respectively, and the maximum speed tracking errors are 0.007 and 0.036 m·s-1, respectively. After four iterative periods, the sliding mode periodic adaptive controller has accurately estimated the given ramp resistance. When the control system is disturbed, the position and speed tracking curves under the sliding mode periodic adaptive controller and PID controller both fluctuate. However, compared with those under the PID controller, the tracking curves under the sliding mode periodic adaptive controller have a smaller fluctuation. Therefore, the proposed SMPALC method can improve the operation control performance of medium-speed maglev trains.

     

    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    LEE H W, KIM K C, LEE J. Review of maglev train technologies[J]. IEEE Transactions on Magnetics, 2006, 42(7): 1917-1925. doi: 10.1109/TMAG.2006.875842
    [2]
    XIONG Jia-yang, DENG Zi-gang. Research progress of high-speed maglev transit[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 177-198. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.01.008
    [3]
    MA Wei-hua, LUO Shi-hui, ZHANG Min, et al. Research review on medium and low speed maglev vehicle[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 199-216. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.01.009
    [4]
    ZHU Li, HE Ying, YU F R, et al. Communication-based train control system performance optimization using deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 10705-10717. doi: 10.1109/TVT.2017.2724060
    [5]
    YU Jin, HE Zheng-you, QIAN Qing-quan, et al. Adaptive fuzzy control of train operation[J]. Journal of the China Railway Society, 2010, 32(4): 44-49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201004009.htm
    [6]
    YU Qiong-xia, HOU Zhong-sheng, XU Jian-xin. D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains[J]. IEEE Transactions on Control Systems Technology, 2018, 26(2): 652-663. doi: 10.1109/TCST.2017.2692730
    [7]
    HE Zhi-yu, YANG Zhi-jie, LYU Jing-yang. Braking control algorithm for accurate train stopping based on adaptive fuzzy sliding mode [J]. China Railway Science, 2019, 40(2): 122-129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201902017.htm
    [8]
    YANG Yan-fei, CUI Ke, LYU Xin-jun. Combined sliding mode and PID control of automatic train operation system[J]. Journal of the China Railway Society, 2014, 36(6): 61-67. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201406014.htm
    [9]
    WU Yan, SHI Hong-bao. Automatic subway train operation control based on neural network and other intelligent control methods[J]. Journal of the China Railway Society, 2000, 22(3): 10-15. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB200003003.htm
    [10]
    LI Zhong-qi, XU Jian. High-speed EMU parking method based on improved fuzzy PID-Smith controller[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 145-154. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.04.011
    [11]
    DONG Hai-rong, GAO Bing, NING Bin, et al. Fuzzy-PID soft switching speed control of automatic train operation system[J]. Control and Decision, 2010, 25(5): 794-796, 800. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201005033.htm
    [12]
    ZHU Jin-quan, GE Qiong-xuan, ZHANG Bo, et al. Traction control strategy of high-speed maglev considering the influence of suspension system[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3087-3096. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS202212016.htm
    [13]
    JI Hou-ji, LIU Yao-zong, XIE Xin-li. Dynamics modeling and analysis of bogie of medium-speed maglev trains with mid-mounted linear motor[J]. Journal of the China Railway Society, 2020, 42(9): 33-38. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202009005.htm
    [14]
    NI Fei, MU Si-yuan, KANG Jin-song, et al. Robust controller design for maglev suspension systems based on improved suspension force model[J]. IEEE Transactions on Transportation Electrification, 2021, 7(3): 1765-1779.
    [15]
    JEONG J H, HA C W, LIM J, et al. Analysis and control of electromagnetic coupling effect of levitation and guidance systems for semi-high-speed maglev train considering current direction[J]. IEEE Transactions on Magnetics, 2017, 53(6): 1-4.
    [16]
    SUN You-gang, XU Jun-qi, QIANG Hai-yan, et al. Adaptive neural-fuzzy robust position control scheme for maglev train systems with experimental verification[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8589-8599.
    [17]
    ZHANG Jin-hui, CHEN Duan-duan, SHEN Gang-hui, et al. Disturbance observer based adaptive fuzzy sliding mode control: a dynamic sliding surface approach[J]. Automatica, 2021, 129: 109606.
    [18]
    INCREMONA G P, RUBAGOTTI M, FERRARA A. Sliding mode control of constrained nonlinear systems[J]. IEEE Transactions on Automatic Control, 2017, 62(6): 2965-2972.
    [19]
    WANG Xi, ZHU Li, WANG Hong-wei, et al. Robust distributed cruise control of multiple high-speed trains based on disturbance observer[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 267-279.
    [20]
    WANG Xi, LI Shu-kai, SU Shuai, et al. Robust fuzzy predictive control for automatic train regulation in high-frequency metro lines[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(6): 1295-1308.
    [21]
    LI De-cang, MENG Jian-jun, XU Ru-xun, et al. Sliding mode adaptive robust H∞ control method for high-speed train under strong wind conditions[J]. Journal of the China Railway Society, 2018, 40(7): 67-73. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201807012.htm
    [22]
    LI Ping, ZHU Guo-li. Robust internal model control of servo motor based on sliding mode control approach[J]. ISA Transactions, 2019, 93: 199-208.
    [23]
    CHEN S Y, GONG S S. Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control[J]. Mechanical Systems and Signal Processing, 2017, 94: 111-128.
    [24]
    JIN Hong-yan, ZHAO Xi-mei. Speed control of permanent magnet linear synchronous motor based on complementary sliding mode control and iterative learning control[J]. Control Theory and Applications, 2020, 37(4): 918-924. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY202004026.htm
    [25]
    LIN Chuan-kai. Nonsingular terminal sliding mode control of robot manipulators using fuzzy wavelet networks[J]. IEEE Transactions on Fuzzy Systems, 2006, 14(6): 849-859.
    [26]
    HOU Ming-dong, WANG Yin-song. Data-driven trajectory tracking sliding mode constraint control for wheeled mobile robot[J]. Control and Decision, 2020, 35(6): 1353-1360. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202006010.htm
    [27]
    YIN Xiu-xing, PAN Li, CAI Shi-bo. Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators[J]. Robotics and Computer-Integrated Manufacturing, 2021, 72: 101884.
    [28]
    LEE J H, SONG J Y, KIM J W, et al. Distance-based intelligent particle swarm optimization for optimal design of permanent magnet synchronous machine[J]. IEEE Transactions on Magnetics, 2017, 53(6): 1-4.
    [29]
    LI Cheng, WANG Xiao-min. An ATO control strategy based on particle swarm optimization[J]. Journal of the China Railway Society, 2017, 39(3): 53-58. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201703010.htm
    [30]
    LUO Jing, HU Wei, LIU Yu-xiang. Traction characteristics analysis and calculation of mid-low speed maglev trains [J]. Electric Locomotives and Mass Transit Vehicles, 2010, 33(6): 21-22. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DJJI201006008.htm
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (487) PDF downloads(70) Cited by()
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return