Iterative Learning Control for Freeway Traffic Distributed Parameter Systems

Abstract

Abstract:

Based on the freeway traffic distributed parameter system model, this paper presents the boundary control approach of the onramp distributed parameter system with consideration of the repeatability feature of traffic system. The approach with feedback PDtype control law improves the precision of the control system by learning useful knowledge in previous conductions. Moreover, it can effectively suppress the influence of the system measurement noises and disturbances. It also integrates the system space and time control information and adopts the freeway traffic distributed parameter system model description, so as to improve the control performance of the whole freeway section. The simulation results further confirm the effectiveness and appliciability of the proposed approachs by Matlab.

Key words: intelligent transportation, distributed parameter system model, iterative learning control, on-ramp metering, boundary control 

摘要：

本文基于快速路交通流分布参数系统模型，利用快速路交通流系统具有重复运行的显著特点，设计一种新的入口匝道分布参数迭代学习边界控制方案.所提出的方法采用带有反馈的PD-型控制律，不仅可以从以前重复过程中学习有用的知识，提高系统的控制精度；还可以有效的抑制当前运行过程中系统的量测噪声和扰动等不确定影响因素.该方法的另一个显著特点在于充分利用了快速路交通系统的分布参数模型描述，更全面地利用了系统的时空控制信息，从而提高了整个路段的控制性能.本文基于Matlab仿真进一步说明所提出方法的有效性和适用性.

关键词: 智能交通, 分布参数系统, 迭代学习控制, 入口匝道控制, 边界控制

CLC Number:

TP273

CHI Rong-hu, LI Jian-ying, LIU Xiang-peng, SUI Shu-lin. Iterative Learning Control for Freeway Traffic Distributed Parameter Systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(2): 42-47.

池荣虎，李建营，刘祥鹏，隋树林. 快速路交通分布参数系统的迭代学习控制方法[J]. 交通运输系统工程与信息, 2013, 13(2): 42-47.

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http://www.tseit.org.cn/EN/Y2013/V13/I2/42

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