Robust Curvature Preview Drift Control Using STA-ISMC under Parameter Uncertainty

Autonomous vehicles exhibit extremely strong nonlinearity during drift. However, existing autonomous drift algorithms often neglect previewed path curvature and offer only limited consideration of road surface uncertainty because of the influence of vehicle nonlinear dynamics, which can affect tracking accuracy and robustness of drift control. To solve these problems, this study proposes a robust optimal drift control framework based on curvature preview. First, a preview vehicle kinematic model is constructed, and a preview model predictive control path-tracking controller that considers the forthcoming curvature is designed. Through the analysis of equilibrium points with additional yaw moment, a robust optimal drift controller is developed, which employs a three-degrees-of-freedom vehicle model with an additional yaw moment. This controller adopts integral sliding mode control with a super-twisting algorithm (STA) and exhibits good stability, which is verified through Lyapunov analysis. The proposed control algorithm is validated through hardware-in-the-loop experiments. The experimental results demonstrate that the proposed method significantly improves path-tracking accuracy and robustness under uncertain road surface conditions, thereby providing an effective control solution for drift-based path-tracking maneuvers.