Volume 23 Issue 2
Apr.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(2): 264-272
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.

     

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