Location Adaptive Model Predictive Controller for Autonomous Vehicle Path Tracking with Location Drifting

With the rapid development of autonomous driving, path tracking has emerged as a pivotal research direction. Model predictive control (MPC) has become one of the most prevailing approaches for path tracking, owing to its superior capacity in dealing with multi-constrained control problems and compatibility with the symmetry of vehicle dynamic systems. Nevertheless, conventional MPC suffers from performance degradation in path tracking when vehicle localization drift occurs, referring to the noticeable deviation between sensor-measured position and actual physical position over time, which is mainly induced by sensor noise and outliers. To overcome these limitations and enhance the accuracy and stability of path tracking, this paper presents a location-adaptive model predictive control framework. Specifically, a supervisor is designed to detect localization drift, and a Runge–Kutta-based location estimator is activated to predict the current vehicle state once drift is identified. Furthermore, a linear time-varying MPC is utilized to compute the desired control input for real-time multi-objective optimization. A set of co-simulations based on Simulink and CarSim are conducted to validate the effectiveness of the proposed strategy. Numerical results demonstrate that the presented method outperforms traditional MPC in terms of tracking accuracy and stability under localization drift conditions.