动态模拟和智能控制技术用于煤矿路头的切割头负载
Junling Feng1, Ye Zhang1, Ying He2
1Department of Automation, Taiyuan Institute of Technology, Taiyuan, China.
PloS one
|March 9, 2026
概括
本研究介绍了煤炭挖掘机的智能控制模型,提高了负载精度和效率. 该模型提高了适应复杂地质条件的适应性,减少了采摘故障,并提高了挖掘性能,以实现更安全的无人采矿操作.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 机器人和控制系统 机器人和控制系统
- 计算力学 计算力学 计算力学
背景情况:
- 煤矿挖掘机因复杂的地质而面临波动负载的挑战.
- 现有的宏观控制器很难捕捉切割上的微观负荷.
研究的目的:
- 开发一种动态模拟和智能控制模型,用于适应性路头机切割头负载.
- 提高捕获微观负载的精度,提高适应复杂工作条件的适应性.
主要方法:
- 有限元素方法 (FEM) 用于微观岩石采集相互作用模拟.
- 使用非主导排序基因算法II (NSGA-II) 进行参数调整的多目标优化.
- 辐射基函数 (RBF) PID控制器结合多体动力学-离散元件方法 (MBD-DEM) 和近距离政策优化 (PPO) 进行负载控制.
主要成果:
- 在普通公路上,高精度挖掘具有最小的路径规划错误.
- 在能源消耗比率和利用效率上有显著的改进.
- 有效的早期预警煤石硬度变化和快速恢复负载稳定性.
- 试验应用显示模拟和测量负载之间的相对误差<6.5%.
结论:
- 开发的模型提高了挖掘效率,并减少了采摘失败.
- 为煤矿中减少人力和无人操作提供技术支持.
- 验证了安全和高效的煤炭行业升级的实际可行性.
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