This paper addresses the crucial aspect of position error modeling and compensation in industrial servomechanisms with the aim to achieve accurate control and high-performance operation in industrial robots and automated production systems. The inherent complexity and nonlinear behavior of these modules, usually consisting of a servomotor and a speed reducer, often challenge traditional analytical modeling approaches. In response, the study extensively explores the design and implementation of Machine Learning (ML) algorithms to obtain a comprehensive model of the Transmission Error (TE) in rotating vector reducers, which is a main source of robot motion accuracy errors. The ML models are trained with experimental data obtained from a special purpose test rig, where the reducer is tested under different combinations of input speed, applied load and oil temperature. In the second part of the work, the resulting predictive model, tailored to capture the intricate dynamics of the analyzed reducer, is imported into a programmable logic controller to enable online compensation strategies during the execution of custom motion profiles. Experimental tests are conducted using two distinct motion profiles: one generated with a cycloidal law, typical of industrial machinery, and the other extrapolated from the joints of an industrial robot during a pick-and-place task. The results demonstrate the effectiveness of the proposed approach, enabling accurate prediction and substantial reductions (over 90%) in the overall reducer TE through the implemented predictive model.

中文翻译：

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使用机器学习方法对工业伺服机构进行在线运动精度补偿


本文讨论了工业伺服机构中位置误差建模和补偿的关键方面，旨在实现工业机器人和自动化生产系统的精确控制和高性能操作。这些模块通常由伺服电机和减速器组成，其固有的复杂性和非线性行为常常对传统的分析建模方法提出挑战。为此，该研究广泛探索了机器学习（ML）算法的设计和实现，以获得旋转矢量减速器中传输误差（TE）的综合模型，这是机器人运动精度误差的主要来源。机器学习模型使用从专用测试台获得的实验数据进行训练，减速器在输入速度、施加负载和油温的不同组合下进行测试。在工作的第二部分中，将生成的预测模型（专为捕获所分析的减速器的复杂动态而定制）导入到可编程逻辑控制器中，以便在执行自定义运动曲线期间启用在线补偿策略。实验测试使用两种不同的运动轨迹进行：一种是根据工业机械典型的摆线定律生成的，另一种是在拾放任务期间从工业机器人的关节推断出来的。结果证明了所提出方法的有效性，通过实施的预测模型能够准确预测并大幅减少总体减速器 TE（超过 90%）。