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Hysteresis Compensation in Force/Torque Sensor based on Machine Learning

机译：基于机器学习的力/扭矩传感器的滞后补偿

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This paper proposes a method to improve the accuracy of the force/torque (F/T) sensor based on machine learning considering time series data. There are several problems with F/T sensors, one of which is hysteresis. Hysteresis is a factor of error dependent on force history. There have been few researches focusing on hysteresis in an F/T sensor. We solved this problem by considering time series data. Time series data was put into machine learning such as linear regression and Support Vector Regression (SVR). We evaluated this method with an existing high dynamic range F/T sensor. We confirmed that the error decreased in both high and low force ranges. Since there is nonlinearity in hysteresis, we predicted that SVR will be more accurate than linear regression. Linear regression considering time series was better than SVR when loading training data at random intervals and loading test data at constant intervals.
机译：本文提出了一种考虑时间序列数据的机器学习提高力/扭矩（F / T）传感器精度的方法。 F / T传感器有几个问题，其中一个是滞后。滞后是依赖于力历史的错误。少数研究侧重于F / T传感器中的滞后。我们通过考虑时间序列数据来解决这个问题。时间序列数据被投入机器学习，例如线性回归和支持向量回归（SVR）。我们用现有的高动态范围F / T传感器评估了该方法。我们确认在高力范围内误差减少。由于滞后存在非线性，因此我们预测SVR比线性回归更准确。考虑时间序列的线性回归比SVR在随机加载训练数据并以恒定的间隔加载测试数据时更好。

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