矿用无人运输车辆轨迹跟踪控制算法研究

矿用无人运输车辆作业环境恶劣，存在大曲率弯道、坡道等非结构化道路明显特征，对无人化运输控制要求高。为改善PID等传统控制算法适应性问题，提高无人驾驶轨迹跟踪的车辆横纵向控制精度，提出一种纯跟踪与PID结合的多点预瞄横向控制、考虑模糊控制表参数拟合的纵向控制方法，减少控制参数的同时提高算法效果。根据传统控制算法设计基础控制器，结合基础算法优势进行横向与纵向控制算法设计，通过硬件在环仿真和实车测试验证算法的性能。试验结果表明，横向控制算法与斯坦利算法相比，车辆路径跟踪精度有明显改善，纵向控制方面，速度跟随误差<1 km/h，保证了车辆驾驶时的平稳性与舒适性。

Trajectory Tracking Control Algorithm for Unmanned Mining Transportation Vehicles

The operating environment for unmanned mining transport vehicles is challenging, characterized by unstructured roads such as high-curvature bends and slopes, which demand high requirements for unmanned transportation control. To improve the adaptability of traditional control algorithms like PID and to increase the accuracy of both lateral and longitudinal control in unmanned driving trajectory tracking,this study proposes a combined approach. It involves a multi-point preview lateral control method integrating pure pursuit with PID, and a longitudinal control method considering fuzzy control table parameter fitting.This approach is developed to reduce the number of control parameters while improving the algorithm''s effectiveness. Initially, a basic controller is designed using the traditional control algorithm. And then the lateral and longitudinal control algorithms are developed based on the advantages of the basic algorithm.Finally, the performance of these algorithms is verified through hardware-in-the-loop simulation and on-vehicle deployment testing. The experimental results show that compared with the Stanley method, the lateral control algorithm significantly improves vehicle path tracking accuracy. In terms of longitudinal control, the speed tracking error is less than 1 km/h, ensuring the smoothness and comfort of the vehicle''s driving performance.