中文摘要：

为探究高速列车车内气压波动与旅客乘坐舒适性的关系，设计可重复性复现车内气压变化的气压模拟系统。利用Simulink与AMESim软件的联合仿真技术建立系统的仿真模型。针对气压模拟系统的多容耦合特性，提出一种基于大数据思想的迭代学习控制算法，该算法利用系统的历史运行数据对迭代学习控制算法控制输入量的给定初值进行匹配计算，然后在此基础上进行动态迭代学习。仿真结果表明，该算法能够显著提高控制系统收敛速度，改善系统的动态性能。

英文摘要：

In order to research the relationship between the high-speed train inner space air pressure fluctuation and passenger comfort, an air pressure simulation system which could simulate the air pressure fluctuation of high-speed train inner space repetitively was designed. The simulation model of air pressure simulation system was established by using the co-simulation technology of Simulink and AMESim. For the Multi-Volume Coupled characteristics of the air pressure simulation system, a kind of iterative learning control （ ILC） algorithm based on big data was proposed. The algorithm uses the history operation data of the system to calculate the given initial value of ILC algorithm control output firstly, and dynamic iteractive learning is then started on this basis. The simulation results show that the proposed algorithm can improve convergence speed and dynamic performance of the system significantly.