State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China.
Tianjin University of Technology and Education, Tianjin 300350, China.
Comput Intell Neurosci. 2022 Feb 1;2022:8155818. doi: 10.1155/2022/8155818. eCollection 2022.
The measurement of input and output torque of a precision reducer, the core component of an industrial robot, plays a vital role in evaluating the robot's performance. The TMSIS and TMSOS of a vertical cylindrical high-precision reducer detector were designed and investigated in this study to realize the accurate measurement of input and output torque of the reducer. Because a transmission chain connects the torque transducer and the reducer, the characteristics of the inevitable additional torque are analyzed in detail. A torque calibration device is developed to realize the calibration of the torque measurement system. The readings of the torque calibration device are compared with the data of the instrument's torque measurement system to realize the instrument's torque calibration. The improved particle swarm optimization and Levenberg-Marquardt algorithm-based radial basis function neural network is used to compensate for the error of the torque measurement system. The parameters of the RBF neural network are settled according to the characteristics of the additional torque and the torque calibration results. The experimental results show that the torque measurement accuracy of the torque measurement system can reach 0.1% FS after torque calibration and error compensation.
精密减速器作为工业机器人的核心部件，其输入、输出扭矩的测量对评估机器人的性能起着至关重要的作用。本文设计并研究了一种垂直圆柱型高精度减速器检测仪的 TMSIS 和 TMSOS，以实现对减速器输入、输出扭矩的精确测量。由于扭矩传感器与减速器之间通过传动链连接，因此详细分析了必然存在的附加扭矩的特性。开发了一种扭矩校准装置，以实现扭矩测量系统的校准。将扭矩校准装置的读数与仪器扭矩测量系统的数据进行比较，实现仪器的扭矩校准。采用改进的粒子群优化和基于 Levenberg-Marquardt 算法的径向基函数神经网络对扭矩测量系统的误差进行补偿。根据附加扭矩的特性和扭矩校准结果确定 RBF 神经网络的参数。实验结果表明，经过扭矩校准和误差补偿后，扭矩测量系统的扭矩测量精度可达 0.1%FS。
