摘要
为避免高速列车通过隧道时,列车表面的隧道压力波通过车体缝隙以及换气系统等传入车内,影响车内乘客舒适度。该文基于高速列车行驶中海量数据的准周期性以及重复性,建立基于大数据的PD型迭代学习控制系统,通过迭代学习寻求最优换气系统风机工作频率,实时调节风机新风量与废排量来抑制车内压力波动。仿真分析表明:采用基于大数据的PD型迭代学习控制方式使得车内压力波动幅值、最大1 s变化率以及最大3 s变化率呈明显下降趋势,明显优于现有的主动控制(恒定风机频率)方式,能够更加有效地抑制高速列车车内压力波动,提高乘客舒适度。
In order to avoid the tunnel pressure wave generated on the train surface transmit into the interior of the train through its body gap and ventilation system so as to influence the comfort of passengers, when the high-speed train passes through the tunnel. A PD iterative learning control system was established based on big data as well as the quasi-periodicity and repeatability of massive data generated in the running process of high-speed trains. The iterative learning method was applied to work out the optimal working frequency of ventilation fans and the fresh air volume of the ventilation fans were adjusted to restrain the pressure fluctuation inside the train in real-time. The simulation results show that the proposed method has significantly reduced the pressure fluctuation amplitude as well as the maximum 1 s change rate and the maximum 3 s change rate. It is superior to existing active control methods（constant ventilation fan frequency）and more efficient in minimizing the air pressure fluctuation and improving the comfort of passengers.
出处
《中国测试》
CAS
北大核心
2016年第5期93-97,共5页
China Measurement & Test
基金
国家自然科学基金项目(51475387
51375403)
关键词
高速列车
车内压力波动
PD迭代学习控制
准周期性
变频风机
high-speed train
pressure fluctuation inside train
PD iterative learning control
quasiperiodicity
frequency conversion fan
