The task of patrolling,seed sowing and industrial production of wheeled robots is a strongly nonlinear intermittent process.In this paper,a data-driven high-order iterative learning control algorithm is proposed for the path tracking of wheeled mobile robots in repeated running scenes.First,the model of wheeled mobile robot is derived and designed,and the discrete-time model in state space is transformed into linear input/output data model by using iterative dynamic linearization method based on state transition.Second,a high-order iterative optimization objective function is designed to obtain the control law,and the parameter update law is used to estimate the unknown parameters in the linear data model.By using high order learning control method,more control input information of previous iteration is used in the control law to improve the control performance.Finally,the simulation results verify the effectiveness of this method in the trajectory tracking control of wheeled robot.