Research of Hybrid Hysteresis Modeling of Harmonic Reducer based on SDH Model and Neural Network in Series

Xuanju Dang; Kaili Wang; Hui Jiang; Xiru Wu; Xiangwen Zhang; Shijie Tang

doi:10.16578/j.issn.1004.2539.2019.08.001

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Research of Hybrid Hysteresis Modeling of Harmonic Reducer based on SDH Model and Neural Network in Series

Theory·Reserach | 更新时间：2022-09-25

- Research of Hybrid Hysteresis Modeling of Harmonic Reducer based on SDH Model and Neural Network in Series
- Journal of Mechanical Transmission Vol. 43, Issue 8, Pages: 1-5(2019)
- 作者机构：
  
  1.桂林电子科技大学电子工程与自动化学院，广西桂林 541004
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.08.001
  CLC：
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党选举,王凯利,姜辉等.SDH模型与神经网络串联的谐波减速器混合迟滞建模研究[J].机械传动,2019,43(08):1-5.

Dang Xuanju,Wang Kaili,Jiang Hui,et al.Research of Hybrid Hysteresis Modeling of Harmonic Reducer based on SDH Model and Neural Network in Series[J].Journal of Mechanical Transmission,2019,43(08):1-5.
党选举,王凯利,姜辉等.SDH模型与神经网络串联的谐波减速器混合迟滞建模研究[J].机械传动,2019,43(08):1-5. DOI： 10.16578/j.issn.1004.2539.2019.08.001.

Dang Xuanju,Wang Kaili,Jiang Hui,et al.Research of Hybrid Hysteresis Modeling of Harmonic Reducer based on SDH Model and Neural Network in Series[J].Journal of Mechanical Transmission,2019,43(08):1-5. DOI： 10.16578/j.issn.1004.2539.2019.08.001.

摘要

针对柔性环节含有的谐波减速器所表现出的特殊非线性迟滞特性，构建了由SDH模型与神经网络串联的谐波减速器的混合迟滞模型。以输入与输出信号之间具有与谐波减速器迟滞曲线相似迟滞特性的SDH模型为前置模型，以补偿前置模型在描述迟滞特性时存在的误差的非线性动态RBF神经网络作为后置模型，构成了混合迟滞模型，描述谐波减速器迟滞非线性特性。根据所搭建的实验平台，对不同频率输入信号、不同负载状态下获得的数据进行建模，与经典RBF神经网络模型和SDH模型相对比，实验表明，所构造的混合迟滞模型精度高、适应性强。

Abstract

A hybrid hysteresis model consisting of SDH model and neural network in series is constructed for the special nonlinear hysteresis characteristics of the harmonic reducer in the flexible link. The SDH model is used as the pre-model of which hysteresis characteristics between the input and output signals is similar to the hysteresis characteristics of the harmonic reducer. The dynamic RBF neural network which can describe the nonlinearity is used as the post-model to make the hybrid hysteresis model to describe the hysteresis nonlinearity characteristic of the harmonic reducer. The data obtained under different frequency input signals and different load are modeled. Compared with the classical RBF neural network model and SDH model, the experimental results show that the constructed hybrid hysteresis model has high precision and strong adaptability.

关键词

谐波减速器迟滞特性SDH模型神经网络混合模型

Keywords

Harmonic reducerHysteresis characteristicSDH modelNeural networkHybrid model

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