铁道科学与工程学报2016，Vol.13Issue(10)：1886-1890,5.
气压模拟系统大数据迭代学习控制算法研究
Research of big data iterative learning control algorithm for air pressure simulation system
摘要
Abstract
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.关键词
高速列车/气压模拟/迭代学习控制/大数据/收敛速度Key words
high-speed train/air pressure simulation/iterative learning control/big data/convergence speed分类
信息技术与安全科学引用本文复制引用
屈国庆,陈春俊,闫中奎..气压模拟系统大数据迭代学习控制算法研究[J].铁道科学与工程学报,2016,13(10):1886-1890,5.基金项目
国家自然科学基金资助项目(51475387) （51475387）
