Path Tracking Control for Underground Articulated Vehicles with Multi-Timescale Predictive Modeling

To enhance the path-tracking accuracy and control stability of articulated underground vehicles navigating high-curvature tunnels, this paper proposes a novel Multi-Time-Scale Nonlinear Model Predictive Control (MTS-NMPC) strategy. The core innovation lies in its dynamic adaptation of the prediction horizon to simultaneously compensate for the body torsion effects and yaw deviations induced by high-speed cornering. A high-fidelity vehicle dynamics model is first established. Subsequently, an adaptive mechanism is designed to adjust the prediction horizon based on the reference speed and road curvature. Experimental results demonstrate that the proposed MTS-NMPC achieves remarkable reductions of 35% and 17% in the maximum lateral tracking error and heading deviation, respectively, compared to conventional NMPC. It also improves stability by suppressing the velocity fluctuations of the articulated joint. The superior control performance and robustness of our method are further validated through field tests in an underground mine.