Sun, Haobo (Tongji University), Pan, Wei (Tongji University), Wang, Han (Tongji University), Liu, Hanghang (Tongji University), Zhang, Lin (Tongji University), Chen, Hong (Tongji University)

Explicit Predictive Torque Vectoring with Stability-Driven Weight Adaptation and Real-Vehicle Assessment

Scheduled for presentation during the Regular Session "S12b-Safety and Risk Assessment for Autonomous Driving Systems" (WE-EA-T12), Wednesday, November 19, 2025, 14:30−14:50, Broadbeach 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

In this paper, a hierarchical predictive torque vectoring strategy with control weights adaptation is proposed to improve the vehicle dynamic performances, including agility, driving stability and tire anti-skid ability. Based on single-step prediction, multi-objective constrained optimization problems for both upper and lower controllers are established for the optimal torque allocation. The incorporation of single-step prediction imparts distinct mathematical forms to the optimization problems, enabling the derivation of global optimal explicit solutions. Next, a stability region based on Routh-Hurwitz and controllability criteria is estimated to reflect the stability level of vehicle motion. The stability index is then calculated to provide the guidance for dynamical adjustment of control weights, thereby meeting the control requirements under different driving conditions. The closed-loop Pylon Course Slalom Test on the low adhesion roads is conducted as the real-vehicle experiment to assess various control strategies. The test results show that the vehicle dynamic performance under the proposed adaptive torque vectoring strategy has been effectively enhanced.