摘要
针对谐波减速器随负载变化所表现出的负载转矩与扭转角之间的迟滞特性,导致谐波减速器转换精度下降的问题,构建了忆阻迟滞模型与RBF神经网络并联的谐波减速器混合迟滞模型。将忆阻器模型改进成忆阻迟滞模型,用于描述谐波减速器迟滞输出的基本变化规律;借助具有非线性拟合能力的RBF神经网络对谐波减速器迟滞模型与忆阻迟滞模型之间的差值进行补偿。实验数据验证结果表明,与忆阻迟滞模型相比,所构建的混合迟滞模型能有效描述谐波减速器迟滞的突变和非光滑特性,模型验证均方误差为0.06。
Aiming at hysteresis changing with the load between the load torque and torsion angle of the harmonic reducer and a reduction of the conversion accuracy of the harmonic reducer resulting from this,a harmonic reducer with a memristor hysteresis model and an RBF neural network in parallel hybrid hysteresis model is proposed.The memristor model is improved to obtain a memristor hysteresis model,which is used to describe the basic change law of the hysteresis output of the harmonic reducer.The difference between the hysteresis model of the harmonic reducer and the memristor hysteresis model is compensated by the RBF neural network with nonlinear fitting ability.Experimental data verification results show that,compared with the memristor hysteresis model,the constructed hybrid hysteresis model can effectively describe the abrupt and non-smooth characteristics of the hysteresis of the harmonic reducer.The model validation mean square error is 0.06.
作者
党选举
魏芳
Dang Xuanju;Wei Fang(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《机械传动》
北大核心
2022年第3期10-15,139,共7页
Journal of Mechanical Transmission
基金
国家自然科学基金(61863008
61863007)
广西自然科学基金(2016GXNSFDA380001)。
关键词
谐波减速器
迟滞特性
忆阻迟滞模型
神经网络
混合模型
并联结构
Harmonic reducer
Hysteresis characteristic
Memristor hysteresis model
Neural Networks
Hybrid model
Parallel structure
