Lateral Trajectory Tracking of Autonomous Mining Trucks Using MPC with Adaptive Load Compensation

With the advancement of mine intelligence, autonomous driving technology for mining trucks has gradually become a research focus. However, when operating in complex mining environments, mining trucks face challenges in trajectory tracking control due to significant load variations and complex working conditions, leading to insufficient precision and poor dynamic adaptability. To address these issues, this paper proposes a lateral trajectory tracking strategy based on Model Predictive Control (MPC) with adaptive load compensation. The controller updates vehicle parameters in real time based on load conditions, improving prediction accuracy and ensuring robust tracking under both empty and full-load states. This method optimizes control outputs in real time, minimizing the impact of load fluctuations on trajectory tracking accuracy and enhancing the robustness of traditional control methods under complex conditions. To validate the effectiveness of the proposed method, comparative simulation experiments were conducted on a Simulink and TruckSim co-simulation platform. The simulation results demonstrate that the proposed MPC control strategy achieves higher trajectory tracking accuracy and stability under both empty-load and fullload conditions, outperforming traditional control methods.