关键词: 后轮转向；路面附着系数；摩擦系数估算；模型预测控制；车辆动力学；鲁棒性

Abstract: This study aims to address the issues of reduced robustness and insufficient control accuracy in rear-wheel steering (RWS) systems operating on low-adhesion road surfaces. Complex road conditions, such as wet or icy surfaces, can cause abrupt variations in the tire–road friction coefficient, severely impacting the robustness and stability of rear-wheel steering control. To tackle these challenges, this research proposes an active control strategy based on real-time estimation of the dynamic road adhesion coefficient; specifically, it constructs a friction coefficient estimation model using multi-source sensor data and designs an adaptive regulation control strategy for the rear wheel angle to achieve rapid response across various road conditions. Validation through both simulation and physical vehicle testing demonstrates that the proposed control method significantly enhances the lateral stability and handling response of the vehicle under complex and variable road environments, while effectively suppressing the risk of instability. This study provides a theoretical foundation and engineering reference for the adaptive chassis control of intelligent vehicles in complex environments, offering significant practical value for improving the all-weather driving safety capabilities of autonomous driving systems.

Key words: rear-wheel steering; road adhesion coefficient; friction coefficient estimation; model predictive control; vehicle dynamics; robustness