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.