针对城市边界控制问题，提出了一种考虑扰动的交通子区迭代学习边界控制方法。首先，在路网车辆平衡方程中引入扰动项建立离散的宏观交通流模型；其次，基于宏观交通流的重复性特征，设计了边界交叉口的迭代学习控制策略，并证明了在有界的扰动作用下系统的跟踪误差收敛到一个界内；最后，以实际路网为例进行对比仿真实验，结果表明考虑扰动的迭代学习边界控制方法能够有效抑制不同程度的干扰对路网性能的影响，提高路网的交通状况。

Aiming at the problem of urban perimeter control, an iterative learning perimeter control method for traffic sub-region considering disturbances is proposed.Firstly, a discrete macroscopic traffic model is established by introducing disturbance term into the vehicle balance equation of road network.Secondly, based on the repeatability of macroscopic traffic flow, the iterative learning control strategy of boundary intersections is designed, and the tracking error of the system is proved to converge to a boundary under the action of bounded disturbance.Finally, taking the actual road network as an example, the simulation results show that the iterative learning perimeter control method considering disturbances can effectively restrain the influence of different degrees of disturbance on the road network performance and improve the traffic condition of the road network.

国家自然科学基金项目（面上项目，重点项目，重大项目）,中国博士后科学基金，山西省应用基础研究项目