Abstract: Unmanned mining trucks are characterized by significant load variations, leading to substantial dynamic characteristics changes between empty and fully loaded states. To improve the adaptability of unmanned mining trucks under variable load conditions and achieve effective acceleration tracking, this paper proposes a dual-loop fuzzy PID controller. The controller integrates inner-loop acceleration control with outer-loop speed control and establishes fuzzy logic rules for different load conditions to enable online adjustment of PID parameters. This design effectively addresses system changes caused by large load variations, achieving precise longitudinal speed and acceleration tracking. Simulation results demonstrate that the proposed controller exhibits strong adaptability and acceleration tracking performance. Under variable load conditions, the algorithm achieves a maximum speed error of 0.49 km/h (1.6% of the operating speed) and a maximum acceleration error of 0.103 m/s2.This method improves the robustness of unmanned mining trucks under varying load conditions and enables accurate acceleration tracking, providing reliable support for unmanned technology in open-pit mining operations.