ODE‐Driven Neural Networks for Trajectory Tracking of Autonomous Vehicles Under Periodic Noise Suppressed

ABSTRACT Autonomous driving technology is rapidly advancing and expected to revolutionize the transportation industry shortly. Although much research on trajectory tracking tasks has been carried out, it is essential to study how to accomplish assigned tasks under the disturbance of noise, especially for periodic noise, which is inevitably generated by electrical or motor interference in scenarios involving electronic circuits. For example, the suspension system constantly compresses and rebounds as the road surface changes during vehicle operation, generating the periodic noise. This paper establishes a kinematic model for the controlled vehicle and formulates the control problem as an optimization task using model predictive control (MPC). Subsequently, a periodic noise‐suppressed neural network (PNSNN) model is proposed based on ordinary differential equations (ODEs) to achieve vehicle control. It is worth pointing out that the PNSNN model considers periodic noise from the perspective of harmonic expansion, thus effectively eliminating its interference. In addition, convergence analyses are conducted on the PNSNN model both with and without periodic noise. Finally, simulations and experiments validate the effectiveness and stability of the proposed PNSNN model.