定形态隧道压力波激扰下车内压力迭代学习控制

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

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.

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 higherorder 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 higherorder 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.