Because of the high complexity of urban traffic flow, the non-repetitive interference in the road network will degrade the dynamic performance of the iterative learning traffic control system. Therefore, a hybrid control method based on Kalman filter and iterative learning is proposed to further improve the robustness and anti-interference ability of the control system. Firstly, the Kalman filter is used to observe the state of the system, and the optimal state of the system is estimated under the condition of random noise. Secondly, an iterative learning control method with forgetting factor is designed, which can enhance the antiinterference ability of large disturbance, and then the reference trajectory of the system is gradually tracked by iterative learning. Finally, the convergence of the algorithm is proved mathematically, and the simulation results also show that the proposed method can reduce the influence of interference on the control system in the disturbance environment, and improve the road capacity and reduce traffic congestion to a certain extent.