基于动态稳定边界的智能车辆路径跟踪控制方法

Intelligent vehicle path tracking control is crucial for ensuring driving safety and stability. Variable speed and road conditions affect the vehicle’s states, and current path tracking methods, which trigger braking based on conservative stability conditions, aim to maintain lateral stability during path tracking. However, these methods tend to degrade path tracking performance under extreme conditions. To address this issue, an elliptical geometric model was applied to represent the dynamic stability region of the vehicle system, and a mapping model between elliptical parameters and driving conditions was established, which allowed for an explicit representation of the dynamic stability region of the vehicle system. Using affine transformations, the constraints for the optimization problem were designed, resulting in a dynamic region of stability integrated path-tracking control method. Verification on a hardware-in-the-loop platform showed that the proposed method reduces vehicle states’ fluctuations caused by braking interventions under high-speed, low-friction conditions, while improving path tracking accuracy by more than 25.5%, and meeting real-time requirements.