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综合塔式起重机动力学:用于系统识别和基于机器学习的控制的轨迹和有效载荷振荡的实验数据集
Ahmad Bala Alhassan1, Syed Muhammad Umair Arif2, Auwal Haruna3
1Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan.
Data in brief
|July 18, 2025
概括
本研究介绍了塔式起重机控制的数据集,重点关注有效载荷振荡和托盘定位. 这些数据有助于开发机器学习模型,以实现高效的起重机自动化和操作.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机械工程 机械工程
- 机器学习应用 机器学习应用
背景情况:
- 起重机对于建筑和航运等行业的材料处理至关重要.
- 由于电缆的灵活性和起重机动力学,控制起重机车的定位和有效载荷的振荡具有挑战性,特别是在塔旋转时.
- 现有的方法难以精确控制,导致效率低下和潜在的损害.
研究的目的:
- 提供塔式起重机轨迹和有效载荷振荡的全面数据集.
- 促进对起重机先进控制策略的研究.
- 能够开发和培训基于机器学习的起重机自动化系统.
主要方法:
- 使用实验室规模的塔式起重机设置来收集数据.
- 模拟了各种操作场景,包括塔的旋转,各种电车轨迹和不同的有效载荷特征 (质量,形状,电缆长度).
- 还记录了有效载荷提升动态.
主要成果:
- 数据集捕获了关于起重机运动的详细信息,以及在各种条件下由此产生的有效载荷振荡.
- 它涵盖了与现实世界起重机应用相关的广泛操作参数.
- 这些数据作为评估控制算法的基准.
结论:
- 生成的数据集是了解塔式起重机动态的宝贵资源.
- 它支持机器学习方法的进步,以加强起重机控制和自动化.
- 这些数据可以导致更高效,更安全的物料处理操作.
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