光学 精密工程, 2019, 27 (3): 694, 网络出版: 2019-05-30
工业机器人谐波减速器迟滞特性的神经网络建模
Neural network modeling of hysteresis for harmonic drive in industrial robots
谐波减速器 迟滞特性 径向基函数神经网络 混合模型 摩擦 harmonic drive hysteresis Radial Basis Function(RBF) neural networks hybrid model friction
摘要
谐波减速器中柔性环节与传动的非线性摩擦, 导致谐波传动出现了不可避免地影响传动精度的复杂迟滞特性, 为了描述谐波减速器的迟滞特性, 本文构建了一个结构简洁的神经网络迟滞混合模型。该模型由类迟滞特性预处理环节和动态RBF神经网络两部分组成: 对输入信号进行类迟滞预处理, 处理后的信号与输入信号之间具有类迟滞特性; 充分利用动态RBF神经网络实现类迟滞到谐波减速器迟滞特性的高精度映射。根据本文搭建的实验平台, 在不同实验条件下获得的数据进行建模验证, 在不同频率输入信号、不同负载, 实现相同建模精度下,神经网络迟滞混合模型的验证精度为0.449 6(MSE), 远高于经典RBF神经网络模型的3.032 1(MSE)精度, 证明了所构造的神经网络迟滞混合模型的有效性和适应性。
Abstract
Nonlinear friction caused by the flexible link and the transmission process in the harmonic drive leads to complex hysteresis characteristics of harmonic transmission that inevitably affect the transmission accuracy. To describe the hysteresis characteristics of the harmonic drive, a concise neural network hysteresis hybrid model, comprising hysteresis-like characteristic preconditioning in series with a dynamic neural network, was presented in this study. It was executed in two steps: the input signal was preprocessed to produce hysteresis-like behavior; the dynamic Radial Basis Function (RBF) neural network was fully utilized to achieve high-precision approximation of hysteresis-like to hysteresis characteristics of the harmonic drive. Moreover, an experimental platform was constructed in this study, and the data obtained under different experimental conditions were modeled and verified. Both at a constant input accuracy and the accuracy with different input signals and loads, the verification accuracy obtained by the neural network hysteresis hybrid model is 0.449 6 (Mean Square Error (MSE)), which is much higher than the 3.0321 (MSE) accuracy of the classical neural network model. This proves the effectiveness and adaptability of the proposed neural network hysteresis hybrid model.
党选举, 王凯利, 姜辉, 伍锡如, 张向文. 工业机器人谐波减速器迟滞特性的神经网络建模[J]. 光学 精密工程, 2019, 27(3): 694. DANG Xuan-ju, WANG Kai-li, JIANG Hui, WU Xi-ru, ZHANG Xiang-wen. Neural network modeling of hysteresis for harmonic drive in industrial robots[J]. Optics and Precision Engineering, 2019, 27(3): 694.