Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form
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摘要:
同一列车重复通过同一隧道时所产生隧道压力波激扰具有形态相似、变尺度变幅值的特性. 针对现有控制策略未考虑这一定形态特性的问题，提出一种基于高阶反馈遗忘迭代学习的控制方法. 首先，建立高速列车车内外气压传递数学模型，并利用实测车内外压力数据进行修正与验证；其次，通过控制列车通风设备的阀门来减缓车内压力变化，提出阶反馈遗忘迭代学习控制算法，并设计变幅值和变尺度处理方法；最后，利用实测压力波生成一组定形态的随机压力波，并进行仿真分析. 仿真结果表明：在重复定形态的隧道压力波激扰下，高阶反馈遗忘迭代学习控制算法能够使车内压力在第8个迭代周期后1 s变化率基本收敛到200 Pa/s以下，而且均方根误差也在第4个迭代周期后降低到15.0000%以下.
Abstract: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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图 7 2种控制方法的压力1 s变化率范数
Figure 7. Norm of pressure change rate within one second for two control methods
表 1 模拟与实测车内气压的MAPE值
Table 1. MAPE values of simulated and measured pressure inside the train
q 1 2 3 4 MAPE/% 0.37 0.31 0.20 0.25 
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表 2 参数坐标点
Table 2. Coordinates of parameters
算法 峰值点坐标 高阶遗忘 ILC （2, 18.1035）、（8, 11.3191）、（13, 2.3886） 传统 P 型 ILC （2, 23.6218）、（8, 20.7152）、（13, 6.2149）
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[1] 田红旗. 列车空气动力学[M]. 北京: 中国铁道出版社, 2007. [2] RICCO P, BARON A, MOLTENI P. Nature of pressure waves induced by a high-speed train travelling through a tunnel[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2007, 95(8): 781-808. doi: 10.1016/j.jweia.2007.01.008 [3] 王宗昌. 主动式与被动式车内压力保护系统对比分析[J]. 铁道车辆,2017,55(6): 30-32,5.WANG Zongchang. Comparison analysis of the active and passive pressure protection systems inside cars[J]. Rolling Stock, 2017, 55(6): 30-32,5. [4] 李树典,周新喜. CRH2型200 km/h动车组车内压力波动控制研究[J]. 机车电传动,2009(2): 6-7.LI Shudian, ZHOU Xinxi. Study on the control of pressure fluctuation in CRH2 type 200 km/h EMUs[J]. Electric Drive for Locomotives, 2009(2): 6-7. [5] 孙明轩, 黄宝健. 迭代学习控制[M]. 北京: 国防工业出版社, 1999. [6] UCHIYAMA M. Formation of high-speed motion pattern of a mechanical arm by trial[J]. Transactions of the Society of Instrument and Control Engineers, 1978, 14(6): 706-712. doi: 10.9746/sicetr1965.14.706 [7] ARIMOTO S, KAWAMURA S, MIYAZAKI F. Bettering operation of robots by learning[J]. Journal of Robotic Systems, 1984, 1(2): 123-140. doi: 10.1002/rob.4620010203 [8] LUO A, XU X Y, FANG L, et al. Feedback-feedforward PI-type iterative learning control strategy for hybrid active power filter with injection circuit[J]. IEEE Transactions on Industrial Electronics, 2010, 57(11): 3767-3779. doi: 10.1109/TIE.2010.2040567 [9] SAAB S S, VOGT W G, MICKLE M H. Learning control algorithms for tracking “slowl” varying trajectories[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1997, 27(4): 657-670. doi: 10.1109/3477.604109 [10] SHEN D, ZHANG W, XU J X. Iterative Learning Control for discrete nonlinear systems with randomly iteration varying lengths[J]. Systems & Control Letters, 2016, 96: 81-87. [11] 王晶,周楠,王森,等. 随机变批次长度的反馈辅助PD型量化迭代学习控制[J]. 控制与决策,2021,36(10): 2569-2576.WANG Jing, ZHOU Nan, WANG Sen, et al. Feedback-assisted PD-type quantized iterative learning control with randomly iteration varying lengths[J]. Control and Decision, 2021, 36(10): 2569-2576. [12] 陈春俊,聂锡成,唐猛. 车外空气压力作用下的CRH2型动车组车内空气压力传递函数模型[J]. 中国铁道科学,2013,34(4): 84-88.CHEN Chunjun, NIE Xicheng, TANG Meng. Transfer function model of the air pressure inside CRH2 EMU under outside air pressure[J]. China Railway Science, 2013, 34(4): 84-88. [13] 冯永平. 隧道压力波作用下车内压力波动因素及动态气密性研究[D]. 成都: 西南交通大学, 2020. [14] CHEN C J, HE Z Y, FENG Y P, et al. Semi-empirical model of internal pressure for a high-speed train under the excitation of tunnel pressure waves[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2022, 236(7): 803-815. doi: 10.1177/09544097211042721 [15] 冯永平,陈春俊,陈仁涛. 基于流固耦合的隧道压力下车体变形对车内压力的影响分析[J]. 机车电传动,2021(3): 80-85.FENG Yongping, CHEN Chunjun, CHEN Rentao. Influence of vehicle body deformation on vehicle interior pressure under tunnel pressure based on fluid-solid coupling[J]. Electric Drive for Locomotives, 2021(3): 80-85. [16] HE Z Y, CHEN C J, WANG D W, et al. Iterative learning control of air pressure variation inside a high-speed train under the excitation of the tunnel pressure wave with a fixed-morphologic form[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2022, 236(6): 637-648. doi: 10.1177/09544097211032742 [17] SUZUKI H. Study on tinnitus caused by pressure fluctuation inside carriage[J]. Foreign Rolling Stock, 1999(5): 15-18. [18] KIM Y T, LEE H, NOH H S, et al. Robust higher-order iterative learning control for a class of nonlinear discrete-time systems[C]//IEEE International Conference on Systems, Man and Cybernetics. Washington D. C.: IEEE, 2003: 2219-2224. [19] WANG H B, DONG J A, WANG Y L. High order feedback-feedforward iterative learning control scheme with a variable forgetting factor[J]. International Journal of Advanced Robotic Systems, 2016, 13(3): 95.1-95.7. [20] YANG H, LI S M. PD-type ILC algorithm research with forgetting factor for a class of linear systems with multiple time delays[J]. Applied Mechanics and Materials, 2012, 220/221/222/223: 1125-1130. -
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