Adaptive Preview Path Tracking Algorithm for Vehicles Considering Curvature Disturbance

Path tracking control played a vital role in unmanned vehicle motion control, and the control performance was highly sensitive to road curvature rapid variations. Therefore, an adaptive preview path tracking control method was proposed with curvature disturbance suppression to enhance the robustness and accuracy of the systems under complex road conditions. By embedding curvature disturbances into the state vector, an augmented LQR problem was formulated and efficiently solved using a reduced-order Riccati equation. Subsequently, the feasibility of the preview controller was verified through frequency-domain response analyses. Furthermore, an adaptive strategy was designed for adjusting preview steps to dynamically optimize the preview distances based on vehicle speeds and the curvatures at the distant preview points. Hardware-in-the-loop simulations and real-vehicle tests confirm the method's effectiveness in improving tracking accuracy and driving stability.