Intelligent Driving Vehicle Trajectory Tracking Control Based on an Improved Fractional-Order Super-Twisting Sliding Mode Control Strategy

Abstract

Aiming at resolving trajectory tracking control challenges during high-speed lane changes in intelligent driving vehicles, an innovative fractional-order sliding mode control approach is introduced in the present study. The control strategy comprises upper and lower-level controls. First, the upper-level control designs the vehicle trajectory tracking controller, integrating a non-singular terminal sliding mode (NTSM) surface with a fractional-order fast super-twisted sliding mode control (FOF-STSMC) algorithm. The NTSM surface properties ensure rapid convergence of the system tracking error to zero within a finite time, while the fractional-order control extends the control system's regulation range and enhances algorithm flexibility. Additionally, the integration with the super-twisting algorithm effectively mitigates oscillation issues in the control input, achieving a smooth input. Second, the lower-level control aims to enhance vehicle driving stability. Utilizing the reference yaw rate, and sideslip angle and accounting for tire force saturation, a fractional-order sliding mode control (FOSMC) algorithm is developed to compute the external yaw moment. Through dynamic load allocation, considering the vertical load for each tire, intelligent external yaw moment distribution significantly improves vehicle stability. Finally, the results of the Carsim–Simulink co-simulation demonstrate that, compared to the STSMC strategy, the FOSMC strategy with front-wheel-only steering, and the linear quadratic regulator (LQR) control strategy, the proposed control strategy in this paper reduces the tracking error by 77%, 61%, and 58%, respectively, achieving more precise and stable trajectory tracking under high-speed conditions.