基于CEEMDAN-TPE-LightGBM-APC算法的直驱转盘的动态错误建模和预测补偿
Manzhi Yang1, Hao Ren1, Shijia Liu1
1College of Mechanical Engineering, Xi'an University of Science and Technology, No. 58 Yanta Middle Road, Xi'an 710054, China.
Micromachines
|July 30, 2025
概括
这项研究引入了一种用于直驱转盘的新型动态错误补偿模型,通过精密机械系统的先进分解和机器学习预测技术显著提高定位准确性.
科学领域:
- 机械工程 机械工程
- 控制系统工程 控制系统工程
- 信号处理 信号处理
背景情况:
- 直接驱动的转盘对于高精度的宏微驱动系统至关重要.
- 这些转盘的定位精度对于整体系统性能至关重要.
- 准确的错误预测和补偿对于先进的控制策略至关重要.
研究的目的:
- 为直接驱动的转盘开发一个动态的连续错误补偿模型.
- 为了提高直驱转盘的定位准确度.
- 为了能够在线预测和纠正定位错误.
主要方法:
- 采用了"分解-建模-整合-纠正"的策略.
- 用自适应噪声 (CEEMDAN) 进行完整的综合实证模式分解分解了历史错误数据.
- 树结构帕森估计器 (TPE) 优化的光梯度增强机 (LightGBM) 模型预测了组件错误,随后进行了自适应预测校正 (APC).
主要成果:
- 补偿定位错误范围在测试和推断测试组中显著减少.
- 定位错误的标准偏差减少了71.2% (测试组) 和61.6% (外推测试组).
- 该模型表现出高度的灵活性,适应性和在线预测-校正能力.
结论:
- 提出的动态连续错误补偿模型有效地提高了直驱转盘的准确性.
- 该方法保持了预测稳定性和运营效率.
- 这项研究对精密机械系统的错误补偿具有重要的理论和实践价值.
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