To realise the accurate and stable longitudinal control of the vehicle manipulated by the driving robot (DRV) under different test conditions, a longitudinal robust dynamic programming (RDP) control method for the DRV based on performance self-learning is proposed. Firstly, the dynamics of the driving robot and the vehicle are analysed. Then, the adjoint sensitivity method is used for the DRV to train the neural ordinary differential equation (NODE). The performance of the vehicle under different pedal openings is learned. A RDP controller is designed to solve the nominal target acceleration. Moreover, the unscented Kalman filtering (UKF) method is used to estimate the uncertain external disturbance of the DRV. Furthermore, dynamic compensation is used for the driving robot to track the vehicle's speed. Finally, the stability of the controller is proved. Compared with MPC, LQR, and PID methods, test results show the effectiveness of the proposed method.