摘要
已有的边界控制方法主要是基于模型的反馈控制算法,其实际应用效果受制于模型参数的标定和环境的影响.迭代学习控制以完全跟踪为目标,仅利用较少的模型信息就可以沿迭代轴实现对系统期望输出的完全跟踪.基于城市交通流的重复特性,提出一种城市交通区域的迭代学习边界控制方法,给出跟踪误差收敛性分析.以日本横滨区域为对象分别进行3种场景的仿真:早高峰、晚高峰和中心区域拥堵.仿真结果表明,迭代学习控制方法对于各种场景下的区域路网交通均能达到较为理想的控制效果.
At present, macroscopic fundamental diagram（MFD）-based perimeter control methods are mostly based on the feedback control algorithm, and their practical application are susceptible to environment. Iterative learning control（ILC） can be used in repetitive regional perimeter control of urban traffic with the features of tracking completely.Therefore, based on the repetitive nature of urban traffic flow, an iterative learning perimeter control for an urban region is presented, and the convergence of tracking error is analyzed. Three scenarios, namely, morning and evening peak,central area congestion, and inhomogeneous cell, are simulated. The results show that the ILC method for road network can obtain ideal control effects under different scenarios.
作者
金尚泰
丁莹
殷辰堃
侯忠生
JIN Shang-tait, DING Ying, YIN Chen-kun, HOU Zhong-sheng(School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, Chin)
出处
《控制与决策》
EI
CSCD
北大核心
2018年第4期633-638,共6页
Control and Decision
基金
国家自然科学基金项目(61573054
61433002
61403025)
关键词
边界控制
宏观基本图
迭代学习控制
perimeter control
macroscopic fundamental diagram
iterative learning control
