• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
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Volume 59 Issue 1
Jan.  2024
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Article Contents
CHEN Chunjun, CAO Yuxiao, HE Zhiying, YANG Lu. Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 20-28. doi: 10.3969/j.issn.0258-2724.20211026
Citation: CHEN Chunjun, CAO Yuxiao, HE Zhiying, YANG Lu. Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 20-28. doi: 10.3969/j.issn.0258-2724.20211026

Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form

doi: 10.3969/j.issn.0258-2724.20211026
  • Received Date: 15 Dec 2021
  • Rev Recd Date: 02 Jun 2022
  • Available Online: 15 Nov 2023
  • Publish Date: 09 Jun 2022
  • The tunnel pressure wave excitation generated when the same train passes through the same tunnel repeatedly has the characteristics of similar forms, variable scales, and variable amplitudes. Since the current control strategies do not take into account this fixed form, an iterative learning control method based on higher-order feedback forgetting was proposed, so as to control the interior pressure fluctuation under the disturbance of the pressure wave of the tunnel with a fixed form. First, a mathematical model of air pressure transmission inside and outside high-speed trains was established, and the measured pressure data inside and outside the train were used for correction and verification. Secondly, pressure changes in the train were mitigated by controlling the valve of the train’s ventilation. The iterative learning control algorithm based on higher-order feedback forgetting was proposed, and the variable amplitude and variable scale processing methods were designed. Finally, a set of stochastic pressure waves with a fixed form was generated by using the measured pressure waves, and simulation analysis was carried out. The simulation results show that the iterative learning control algorithm based on higher-order feedback forgetting can make the pressure inside the train converge to below 200 Pa/s within 1 s after the 8th iteration period, and the RMSE value is reduced to below 15.0000% after the 4th iteration period under the excitation of the tunnel pressure wave with repetitive fixed form.

     

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