It is important to reveal the inverse dynamics of manipulators to improve control performance of model-based control. Neural networks (NNs) are promising techniques to represent complicated inverse dynamics while they require a large amount of motion data. However, motion data in dead zones of actuators is not suitable for training models decreasing the number of useful training data. In this study, based on the fact that the manipulator joint does not work irrespective of input torque in dead zones, we propose a new loss function that considers only errors of joints not in dead zones. The proposed method enables to increase in the amount of motion data available for training and the accuracy of the inverse dynamics computation. Experiments on actual equipment using a three-degree-of-freedom (DOF) manipulator showed higher accuracy than conventional methods. We also confirmed and discussed the behavior of the model of the proposed method in dead zones.

翻译：揭示机械臂逆动力学对提升基于模型的控制性能至关重要。神经网络（NNs）是表征复杂逆动力学的有效技术，但需要大量运动数据。然而，执行器死区内的运动数据并不适合训练模型，这导致可用训练数据减少。本研究基于机械臂关节在死区内输入扭矩无效的事实，提出了一种仅考虑非死区关节误差的新型损失函数。该方法可增加可用于训练的运动数据量，并提升逆动力学计算的精度。采用三自由度（DOF）机械臂进行的实际设备实验表明，该方法比传统方法具有更高的精度。我们还验证并讨论了所提方法模型在死区内的行为特性。