Monitoring external disturbing forces is of great significance for improving the control performance and interaction safety of ball screw drives. In consideration of the low cost in long-term use and the non-invasiveness to work space, estimating external disturbing forces using motor torque and motion states has been viewed as the solution that has great potential to be applied in real industry. However, the existing methods cannot efficiently establish accurate estimation models in a low-cost and general manner (i.e., not depend on specific work scenarios). To overcome this challenge, a novel hybrid-driven method that fuses Long Short-Term Memory (LSTM) Neural Network (NN) and Expanded Disturbance Kalman Filter (EDKF) is proposed. The motion state of the worktable is decoupled as the linear superposition of the outputs of two independent dynamic models, i.e., Ideal Dynamic Model (IDM) and Disturbance Dynamic Model (DDM). IDM predicts the motion state that is only driven by the motor torque, is modeled as an LSTM NN and trained by self-excitation experiments. DDM is equivalent to the disturbance transfer function and easily identified by impact tests. The motion state only driven by external disturbing forces is calculated by the predicted result of IDM and the monitored actual motion state, and then external disturbing forces are estimated by a DDM-based EDKF. The verifications in simulation and real environments both show that by the proposed method, accurate estimation models of external disturbing forces can be efficiently established through self-excitation experiments and impact tests.

中文翻译：

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融合 LSTM 神经网络和扩展干扰卡尔曼滤波器来估计滚珠丝杠驱动器的外部干扰力

监测外部干扰力对于提高滚珠丝杠传动的控制性能和交互安全性具有重要意义。考虑到长期使用的低成本和对工作空间的非侵入性，利用电机扭矩和运动状态估计外部干扰力已被视为在实际工业中具有巨大应用潜力的解决方案。然而，现有方法无法以低成本、通用的方式（即不依赖于具体的工作场景）有效地建立准确的估计模型。为了克服这一挑战，提出了一种融合长短期记忆（LSTM）神经网络（NN）和扩展干扰卡尔曼滤波器（EDKF）的新型混合驱动方法。工作台的运动状态解耦为两个独立的动态模型（IDM）和扰动动态模型（DDM）输出的线性叠加。 IDM 预测仅由电机扭矩驱动的运动状态，建模为 LSTM NN，并通过自激实验进行训练。 DDM相当于扰动传递函数，很容易通过冲击试验来识别。通过IDM的预测结果和监测到的实际运动状态计算仅由外部干扰力驱动的运动状态，然后通过基于DDM的EDKF估计外部干扰力。仿真和真实环境的验证均表明，该方法可以通过自激实验和冲击试验有效建立外部扰动力的精确估计模型。