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目前,许多国内外学者开始从宏观层面基于宏观基本图(Macroscopic Fundamental Diagram,MFD)来设计边界控制器以调节路网内的车流量.然而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(Iterative Learning Control,ILC)以完全跟踪为目标,仅利用较少的模型信息就可沿迭代轴实现对系统的完全跟踪.基于城市交通流的重复特性,本文提出了一种城市交通区域的迭代学习边界控制方法,给出了跟踪误差收敛性分析.以日本横滨区域为对象分别进行了三种场景的仿真:早高峰、晚高峰和中心区域拥堵.仿真结果显示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, many domestic and foreign scholars begin to design boundary controllers based on Macroscopic Fundamental Diagram (MFD) to regulate traffic flow in the road network.However, the existing boundary control methods are mainly based on model feedback control algorithm , And its practical application is affected by the calibration of the model parameters and the environment.Iterative Learning Control (ILC) aims at complete tracking, and can track the system along the iteration axis with less model information Based on the repetitive characteristics of urban traffic flow, this paper presents an iterative learning boundary control method for urban traffic area, and gives the convergence analysis of tracking error. Three scenarios are simulated respectively in Yokohama area of Japan: early peak , Late peak and congestion in the central area.The simulation results show that the iterative learning control method can achieve a better control effect for the regional road network traffic in various scenarios.