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受限迭代学习入口匝道控制收敛性分析
  • ISSN号：1009-6744
  • 期刊名称：交通运输系统工程与信息
  • 时间：0
  • 页码：166-173
  • 分类：U491[交通运输工程—交通运输规划与管理;交通运输工程—道路与铁道工程]
  • 作者机构：[1]北京交通大学电子信息工程学院先进控制系统研究所,北京100044
  • 相关基金：国家自然科学基金重点项目（60834001）;国家自然科学基金面上项目（60774022）
  • 相关项目：基于数据驱动的控制理论及在大型复杂系统中的应用
作者： 赵明|侯忠生|晏静文|
关键词： 城市交通, 迭代学习控制, 快速路入口匝道控制, 输入受限, 状态受限, 收敛性, urban traffic, iterative learning control, freeway ramp metering, input constraint, state constraint, convergence
中文摘要：

在实际的快速路交通流系统中,入口匝道的流量和快速路主路的速度都是受限的,因此在对快速路交通流进行控制时考虑这些限制十分必要.基于迭代学习控制的快速路入口匝道控制是近年来快速路控制领域的一个研究热点,然而,至今为止还没有在输入和状态同时受限情况下的相关收敛性分析.本文首先介绍了快速路交通流模型,并将交通密度控制转化为输出跟踪问题;然后通过严格的数学分析证明了在入口匝道流量受限和主路速度受限的情况下,基于迭代学习控制的入口匝道控制仍然能保证交通流密度收敛于期望密度;最后通过仿真研究验证了该方法在受限情况下能达到很好的控制效果.

英文摘要：

In actual freeway traffic system,the on-ramp flow and the mainline speed are constrained.Therefore,it is necessary to take these constraints into consideration in controlling the freeway traffic flow.ILC based freeway ramp metering has become a research hotspot these years,however,there is no corresponding convergence analysis under input and state constraints.In this paper,the traffic density control problem is first formulated into an output tracking problem.Next,with rigorous analysis,the ILC based freeway ramp metering is able to guarantee the asymptotic convergence of the traffic density to the desired one,despite the presence of velocity and input constraints.The control performance under constraints is further verified by intensive simulations.

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期刊信息
  • 《交通运输系统工程与信息》
  • 中国科技核心期刊
  • 主管单位:中国科学技术协会
  • 主办单位:中国系统工程学会
  • 主编：毛保华
  • 地址：北京市海淀区西直门外上园村3号北京交通大学机械工程楼D403室
  • 邮编：100044
  • 邮箱：Bhmao2006@bjtu.edu.cn
  • 电话：010-51684836
  • 国际标准刊号：ISSN：1009-6744
  • 国内统一刊号：ISSN：11-4520/U
  • 邮发代号:82-652
  • 获奖情况:
  • 2004年被国家科技部评定为"中国科技核心期刊"
  • 国内外数据库收录:
  • 荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,中国中国科技核心期刊,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:8131