Lateral stability control for distributed drive autonomous vehicle with extreme path tracking

Abstract

To enhance the path-tracking accuracy and lateral stability of autonomous vehicles under extreme conditions, this paper proposes a coordinated control strategy based on an Active Front Steering (AFS) system and Direct Yaw Moment Control (DYC). First, a path-tracking controller based on Model Predictive Control (MPC) is designed, which includes a Kalman filter to correct lateral stiffness in real-time and calculates the front wheel steering angle output based on path-tracking errors and current direct yaw moment. Next, we formulated the Direct Yaw Moment Control objective as a linear combination of stability and maneuverability objectives, and used a fuzzy controller to dynamically coordinate the weights of stability and maneuverability objectives. This process not only improves the adaptability of the control strategy but also enhances vehicle performance under various road conditions. Furthermore, a Sliding Mode Control (SMC) is employed to calculate the required direct yaw moment. This approach ensures vehicle stability during high-speed driving or changes in road surface adhesion. To optimize torque distribution, we employed a constrained quadratic programming method to minimize tire load rates, thereby improving vehicle energy efficiency and handling. Finally, through Hardware-in-the-Loop (HIL) testing, we validated that the proposed control strategy achieves excellent tracking accuracy, real-time performance, and lateral stability under different road adhesion conditions and vehicle speeds. The control strategy developed in this study not only exhibits theoretical innovation but also demonstrates significant effectiveness in practical applications, providing valuable insights for path-tracking and lateral dynamic control strategies of autonomous vehicles under extreme conditions.