Yang Manzhi, Ren Hao, Liu Shijia, Feng Bin, Wei Juan, Ge Hongyu, Zhang Bin
College of Mechanical Engineering, Xi'an University of Science and Technology, No. 58 Yanta Middle Road, Xi'an 710054, China.
Micromachines (Basel). 2025 Jun 22;16(7):731. doi: 10.3390/mi16070731.
The direct-drive turntable serves as the core actuator in high-precision macro-micro drive systems, where its positioning accuracy fundamentally determines overall system performance. Accurate error prediction and compensation technology represent a critical prerequisite for achieving continuous error compensation and predictive control in direct-drive turntables, making research on positioning error modeling, prediction, and compensation of vital importance. This study presents a dynamic continuous error compensation model for direct-drive turntables, based on an analysis of positioning error mechanisms and the implementation of a "decomposition-modeling-integration-correction" strategy, which features high flexibility, adaptability, and online prediction-correction capabilities. Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. Validation results demonstrate substantial performance improvements, with compensated positioning error ranges reduced from [-31.83″, 41.59″] to [-15.09″, 12.07″] (test set) and from [-22.50″, 9.15″] to [-8.15″, 8.56″] (extrapolation test set), corresponding to standard deviation reductions of 71.2% and 61.6%, respectively. These findings conclusively establish the method's effectiveness in significantly enhancing accuracy while maintaining prediction stability and operational efficiency, underscoring its considerable theoretical and practical value for error compensation in precision mechanical systems.
直接驱动转盘是高精度宏微驱动系统的核心执行器，其定位精度从根本上决定了整个系统的性能。准确的误差预测和补偿技术是在直接驱动转盘中实现连续误差补偿和预测控制的关键前提，因此对定位误差建模、预测和补偿的研究至关重要。本研究基于对定位误差机制的分析和 “分解-建模-集成-校正” 策略的实施，提出了一种直接驱动转盘的动态连续误差补偿模型，该模型具有高度的灵活性、适应性和在线预测校正能力。我们的方法包括四个关键阶段：基于自适应噪声的完全集成经验模态分解（CEEMDAN）对历史误差数据进行分解，使用树状结构帕曾估计器（TPE）优化的轻梯度提升机（LightGBM）算法为每个本征模态函数（IMF）开发特定组件的预测模型，集成组件预测以生成初始值，以及应用自适应预测校正（APC）模块以产生最终预测。验证结果表明性能有显著提升，补偿后的定位误差范围从[-31.83″, 至41.59″]减小到[-15.09″, 至12.07″]（测试集），从[-22.50″, 至9.15″]减小到[-8.15″, 至8.56″]（外推测试集），相应的标准差分别降低了71.2%和61.6%。这些发现确凿地证明了该方法在显著提高精度的同时保持预测稳定性和运行效率方面的有效性，突显了其在精密机械系统误差补偿方面的重要理论和实用价值。
