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<p>The path tracking control of underground Load-Haul-Dump (LHD) is essential for reducing operators’ working time in underground environments and enhancing mining efficiency. Existing NMPC-based controllers often exhibit suboptimal performance, such as oscillatory movements and poor tracking accuracy in large-curvature turns, increasing the risk of collision with tunnel walls. To address these limitations, this study proposes an improved NMPC controller specifically designed to enhance path tracking accuracy and stability under these conditions. Comparative simulations between the proposed controller, traditional NMPCs, and NMPCs enhanced with other optimization algorithms demonstrate significant performance improvements. The proposed strategy reduces the maximum absolute lateral deviation by an average of 55.06% across all tested trajectories, with a peak improvement of 85.19% for a specific trajectory. These results demonstrate that the proposed controller achieves high tracking accuracy and effectiveness under the tested reference trajectories. The finding establishes the proposed controller as a promising solution for optimizing the path tracking of underground vehicles.</p>

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Time-domain optimization based NMPC path tracking control for underground LHD

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

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  • Ya Liu,
  • Pingan Peng,
  • Liguan Wang,
  • Jiaxi Wu,
  • Ru Lei,
  • Mingyu Lei,
  • Chaowei Zhang,
  • Yongqing Zhang

摘要

The path tracking control of underground Load-Haul-Dump (LHD) is essential for reducing operators’ working time in underground environments and enhancing mining efficiency. Existing NMPC-based controllers often exhibit suboptimal performance, such as oscillatory movements and poor tracking accuracy in large-curvature turns, increasing the risk of collision with tunnel walls. To address these limitations, this study proposes an improved NMPC controller specifically designed to enhance path tracking accuracy and stability under these conditions. Comparative simulations between the proposed controller, traditional NMPCs, and NMPCs enhanced with other optimization algorithms demonstrate significant performance improvements. The proposed strategy reduces the maximum absolute lateral deviation by an average of 55.06% across all tested trajectories, with a peak improvement of 85.19% for a specific trajectory. These results demonstrate that the proposed controller achieves high tracking accuracy and effectiveness under the tested reference trajectories. The finding establishes the proposed controller as a promising solution for optimizing the path tracking of underground vehicles.