MPC-based Torque Vectoring for a High-Performance Vehicle with Feasible Yaw Moment Bounds Computation

Torque Vectoring (TV) control is an effective strategy to improve the lateral performance of a vehicle with an additional yaw moment generated by unevenly distributing the torque demand among the individual wheels. However, dangerous total torque over-actuation may occur if the high-level controller requests yaw moments that do not consider individual wheel torque constraints and driver’s longitudinal demand, particularly in powertrains limited to positive torques only. This paper proposes an MPC-based TV architecture in which admissible yaw moment bounds are computed online through a daisy-chain greedy allocation of the driver-requested total torque. The resulting bounds constrain the MPC optimization, ensuring that the downstream torque allocator can satisfy both wheel torque limits and the driver-imposed longitudinal demand. The approach is validated in a high-fidelity simulation environment, showing that the proposed strategy preserves the desired longitudinal behavior by only moderately worsening lateral performance.