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

Dehghan Manshadi, Mohammad; Mashadi, Behrooz

doi:10.22068/ase.2026.751

Volume 16, Issue 2 (6-2026) ASE 2026, 16(2): 5010-5023 | Back to browse issues page

‎ 10.22068/ase.2026.751

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Dehghan Manshadi M, Mashadi B. Coordinated Control of Torque Vectoring & Active Rear Camber for Autonomous Vehicle Path-Tracking under Limit Conditions. ASE 2026; 16 (2) :5010-5023
URL: http://ase.iust.ac.ir/article-1-751-en.html

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

Mohammad Dehghan Manshadi

, Behrooz Mashadi

Abstract: (81 Views)

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 140 km/h maneuver on dry asphalt, the proposed fully integrated system expands the maximum achievable lateral acceleration to 0.8g. Compared to the baseline AFS configuration, it significantly reduces the root-mean-square (RMS) and peak lateral tracking errors by 32% (to 0.239 m) and 27% (to 0.687 m), respectively, while concurrently decreasing the peak steering demand by 27%. Furthermore, under low-friction critical conditions (60 km/h, μ=0.5), 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 74% of the real-time step limit, providing a highly viable and computationally efficient strategy for automotive Electronic Control Unit (ECU) implementation.

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

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Type of Study: Research | Subject: Autonomous vehicles