• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2025, Vol. 61 ›› Issue (5): 263-274.doi: 10.3901/JME.2025.05.263

• 数字化设计与制造 • 上一篇    

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基于非对称多段S型曲线的矿用电铲轨迹优化

龙秀华1,2, 胡正国1,2, 付涛1,2, 连楷研1,2, 宋学官1,2   

  1. 1. 大连理工大学机械工程学院 大连 116024;
    2. 大连理工大学高性能精密制造全国重点实验室 大连 116024
  • 收稿日期:2023-12-28 修回日期:2024-09-26 发布日期:2025-04-15
  • 作者简介:龙秀华,男,1999年出生,硕士研究生。主要研究方向为矿用电铲智能化。E-mail:l15185742897@163.com;宋学官(通信作者),男,1982年出生,博士,教授,博士研究生导师。主要研究方向为多学科耦合建模与优化设计、装备数字孪生与智能化。E-mail:sxg@dlut.edu.cn
  • 基金资助:
    国家自然科学基金(52075068)资助项目。

Trajectory Optimization and Design for Cable Shovel Based on Asymmetric Multi-segment S-curve

LONG Xiuhua1,2, HU Zhengguo1,2, FU Tao1,2, LIAN Kaiyan1,2, SONG Xueguan1,2   

  1. 1. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024;
    2. Dalian University of Technology, State Key Laboratory Hing-performance Precision Manufacturing, Dalian 116024
  • Received:2023-12-28 Revised:2024-09-26 Published:2025-04-15

摘要: 作为露天煤矿开采的关键装备,矿用电铲机体结构复杂且庞大,其具有作业工况多变、作业惯性较大等特点。为了提高挖掘的稳定性和平滑性,降低挖掘能耗,提出了一种基于非对称多段S型曲线速度控制的大型矿用电铲轨迹规划方法。运用解析法推导出铲斗的运动学模型,为后期的轨迹优化提供模型基础。以矿用电铲的铲斗、推杆、提升绳为结构约束,以推压电机和提升电机的功率为性能约束,以最低挖掘能耗为优化目标,运用遗传算法(GA)优化速度曲线的各段控制时间得到最优决策变量,最后将优化所得的决策变量作为输入,代入目标函数进行实验验证。实验结果表明,得到的推杆和提升绳的速度输出没有突变,挖掘力的输出也较为平稳,挖掘轨迹连续且平滑,挖掘能耗均显著低于T型曲线和传统的对称S型曲线速度控制算法,证明了该方法的有效性和优越性。

关键词: 非对称, 多段S型曲线, 轨迹规划, 电铲

Abstract: As a key piece of equipment in open-pit mining, the cable shovel is large and structurally complex. It operates under varying working conditions and exhibits significant operational inertia. To enhance digging stability and smoothness, while reducing energy consumption, this paper proposes a trajectory planning method for cable shovels based on an asymmetric multi-segment S-curve velocity control. The kinematic model of the shovel dipper is derived analytically, providing a foundation for subsequent trajectory optimization. Structural constraints are set for the dipper, dipper handle, and rope, while performance constraints are set for the power of the crowd and hoist motors. The optimization goal is to minimize excavation energy consumption. Using a genetic algorithm (GA), the control times for each segment of the velocity curve are optimized, resulting in the optimal decision variables. The optimized variables are then input into the objective function for experimental validation. Results show that the velocity outputs of the dipper handle and rope exhibit no sudden changes, and the digging force output is stable. The digging trajectory is continuous and smooth. Energy consumption is significantly lower compared to T-curve and traditional symmetric S-curve control algorithms, demonstrating the effectiveness and superiority of the proposed method.

Key words: asymmetric, multi-segment s-curve, trajectory planning, cable shovel

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