Coordinated Control of Torque Vectoring & Active Rear Camber for Autonomous Vehicle Path-Tracking under Limit Conditions

Path-tracking for autonomous vehicles at physical handling limits is severely challenged by nonlinear tire saturation, which degrades conventional Active Front Steering (AFS) systems. This study proposes a hierarchical control architecture coordinating AFS, Torque Vectoring Control (TVC), and Active Rear Camber (ARC) to enhance path-tracking accuracy under limit driving conditions. An upper-level Linear Model Predictive Control (LMPC) algorithm is designed to calculate the virtual corrective yaw moment and the optimal rear camber angle. Simultaneously, a lower-level three-mode Quadratic Programming (QP) framework dynamically allocates torques based on instantaneous tire friction capacities. MATLAB/CarSim co-simulations of severe Double Lane Change (DLC) maneuvers validate the system's efficacy. Quantitatively, during a ۱۴۰ km/h maneuver on dry asphalt, the proposed fully integrated system expands the maximum achievable lateral acceleration to ۰.۸g. Compared to the baseline AFS configuration, it significantly reduces the root-mean-square (RMS) and peak lateral tracking errors by ۳۲% (to ۰.۲۳۹ m) and ۲۷% (to ۰.۶۸۷ m), respectively, while concurrently decreasing the peak steering demand by ۲۷%. Furthermore, under low-friction critical conditions (۶۰ km/h, μ=۰.۵), the controller effectively limits sideslip oscillations and prevents vehicle spin-out. Ultimately, the formulated hierarchical framework manages the over-actuation dynamically, yielding a peak execution time that consumes only ۷۴% of the real-time step limit, providing a highly viable and computationally efficient strategy for automotive Electronic Control Unit (ECU) implementation.

کلیدواژه ها:

Autonomous vehicles ، Model Predictive Control (MPC) ، Active Rear Camber (ARC) ، Torque Vectoring Control (TVC) ، Limit handling ، Control allocation