To address the issue that traditional Nonlinear Model Predictive Control (NMPC), which employs a fixed prediction horizon, struggles to simultaneously satisfy the requirements for both tracking accuracy and real-time performance, this study proposes a Fuzzy control-based Adaptive prediction horizon kinematic NMPC (FANMPC) approach. This method utilizes road curvature and vehicle speed as fuzzy inputs to adjust the prediction horizon in real-time. Based on the vehicle kinematics model, a nonlinear optimization problem is formulated and solved using the Sequential Quadratic Programming (SQP) algorithm. Additionally, control increment constraints and dynamic stability constraints are incorporated to ensure smooth control. Simulation experiments demonstrate that, under both low-speed (30 km/h) and high-speed (70 km/h) double lane change conditions, compared to fixed predictive horizon Model Predictive Control (MPC), the proposed method enhances lateral tracking accuracy by 42.4% and 77.91%, respectively. Furthermore, when compared to fixed prediction horizon NMPC, it improves lateral tracking accuracy by 23.4% and 8.01%, respectively, while reducing the average solution time by 11.72% and 16.04%, respectively. The proposed method significantly enhances the real-time performance of the algorithm while maintaining tracking accuracy, offering an optimized solution for trajectory tracking control in autonomous driving that balances both accuracy and efficiency.
Liu et al. (2026) studied this question.