火车
控制理论(社会学)
趋同(经济学)
迭代学习控制
地铁列车时刻表
终端(电信)
计算机科学
规范(哲学)
类型(生物学)
控制(管理)
模拟
人工智能
电信
地图学
经济增长
生物
操作系统
经济
法学
地理
生态学
政治学
作者
Qiongxia Yu,Xuhui Bu,Ronghu Chi,Zhongsheng Hou
出处
期刊:2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)
日期:2018-05-01
被引量:2
标识
DOI:10.1109/ddcls.2018.8516062
摘要
High-speed trains always operate from the same departure station to the same terminal station and hence iterative learning control (ILC) is an appropriate approach for automatic train control. However, due to complex environment and unknown uncertainties, the train may not arrive at the terminal station on time, or earlier and later than the schedule time in each operation. To address this problem, a modified proportional-type (P-type) ILC is presented where the trial length in each operation can be randomly varying. Moreover, the convergence condition in 2-norm is also derived through rigorous analysis. The effectiveness of the modified P-type ILC is further verified through simulations.
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