Path tracking and stability integrated control for autonomous vehicles based on adaptive robust model predictive control

To enhance the path-tracking performance and stability of autonomous vehicles (AVs), this paper proposes an adaptive robust model predictive control (ARMPC) strategy. First, a path-tracking model is established by integrating a two-degree-of-freedom (2-DoF) vehicle dynamics model with a preview error model. Then, to address model uncertainties caused by variations in tire cornering stiffness, a linear parameter varying (LPV) model with four polytopic vertices is constructed. Subsequently, a stability envelope for vehicle dynamics is defined, specifying the operational boundaries for the sideslip angle and yaw rate. Based on this envelope, a stability index is proposed to quantitatively evaluate vehicle stability, and a weight adaptive mechanism is designed to coordinate the objectives of path tracking and stability control. The min-max optimization problem with adaptive weights and multiple constraints is solved using a robust model predictive control (RMPC) framework based on linear matrix inequality (LMI) to determine the optimal front steering angle. Finally, co-simulation results from Carsim and MATLAB demonstrate that the proposed strategy significantly improves both path-tracking accuracy and vehicle stability under various velocities and road adhesion conditions.