Robust tube model predictive control path tracking framework for distributed drive heavy-duty vehicles on steep slopes under varying payloads and uncertain crosswinds

This paper proposes a robust control framework for Distributed drive heavy-duty vehicles (DDHVs) to enhance vehicle path tracking performance and stability under steep slopes and uncertain crosswind disturbances, while reducing tire wear. Firstly, a robust tube model predictive control (RTMPC)-based path tracking control method is proposed to improve tracking performance and vehicle stability, suppressing external disturbances caused by unpredictable crosswinds and sharp curves on steep slopes for DDHVs. Simultaneously, for longitudinal control, a radial basis function neural network-based sliding mode control (RBFNN-SMC) method is designed to strengthen system robustness and adaptability through dynamic compensation of slope disturbances. Moreover, the integration of a torque distribution module within the framework, in conjunction with an adaptive adjustment module, facilitates tire torque distribution for the total longitudinal force and the target additional yaw moment. By dynamically optimizing the weighting coefficients between tire energy consumption and tire load factor, vehicle stability and dynamic performance are ensured, while achieving balanced distribution of tire slip energy loss. Finally, the effectiveness of the proposed method is evaluated under different load conditions through Trucksim/Simulink co-simulation. The results demonstrate that the proposed control framework exhibits superior robustness and effectiveness in sharp curve and steep slope scenarios compared to traditional MPC control.