The uncertainty of the steer-by-wire (SbW) system and external disturbance during path tracking may lead to lateral position deviation of the vehicle, making it challenging to realize high-precision path tracking control. Therefore, this paper proposes an adaptive neural network controller with an integrated SbW system. Firstly, the SbW system model and vehicle dynamics model are integrated into a strict feedback model. Then, an adaptive neural network approximator and a disturbance observer are designed to estimate the nonlinear friction and the unknown disturbance. Next, based on the backstepping control, a barrier Lyapunov function is introduced to constrain the tracking error, thereby improve the tracking accuracy. Finally, a rigorous stability analysis based on Lyapunov stability theory is carried out to ensure that all signals of the closed-loop system are bounded. The results of numerical simulations, hardware-in-the-loop (HIL) experiments and, real-vehicle experiment show that the proposed method has good control performance. The proposed method achieves a good control effect, with the maximum lateral position error is reduced to 0.18 m in the real-vehicle experiment. The scheme can provide theoretical reference for the control practice of autonomous vehicles.
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