Research on Adaptive Back-stepping Control of Harmonic Drive Based on the RBF Neural Network

Song Gang; Chen Manyi; Qiu Linfeng; Zhang Jie

doi:10.16578/j.issn.1004.2539.2023.08.016

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Research on Adaptive Back-stepping Control of Harmonic Drive Based on the RBF Neural Network

Test·Analysis | 更新时间：2023-08-22

- Research on Adaptive Back-stepping Control of Harmonic Drive Based on the RBF Neural Network
- Journal of Mechanical Transmission Vol. 47, Issue 8, Pages: 116-122(2023)
- 作者机构：
  
  1.武汉理工大学机电工程学院，湖北武汉 430070
  2.武汉理工大学绍兴高等研究院，浙江绍兴 312000
  3.浙江来福谐波传动股份有限公司，浙江绍兴 312000
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.08.016
  CLC：
- Published：15 August 2023，
  
  Received：13 June 2022，
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宋港,陈满意,邱临风等.基于RBF神经网络的谐波传动自适应反演控制研究[J].机械传动,2023,47(08):116-122.

Song Gang,Chen Manyi,Qiu Linfeng,et al.Research on Adaptive Back-stepping Control of Harmonic Drive Based on the RBF Neural Network[J].Journal of Mechanical Transmission,2023,47(08):116-122.
宋港,陈满意,邱临风等.基于RBF神经网络的谐波传动自适应反演控制研究[J].机械传动,2023,47(08):116-122. DOI： 10.16578/j.issn.1004.2539.2023.08.016.

Song Gang,Chen Manyi,Qiu Linfeng,et al.Research on Adaptive Back-stepping Control of Harmonic Drive Based on the RBF Neural Network[J].Journal of Mechanical Transmission,2023,47(08):116-122. DOI： 10.16578/j.issn.1004.2539.2023.08.016.

摘要

由于自身结构上的特点，谐波传动系统存在柔性变形、摩擦和外界不确定干扰等非线性因素。传统控制器大多对系统进行了一定程度的简化，或未考虑非线性外界扰动，导致所设计的控制器性能达不到预期效果。为了提高系统精度，建立了考虑系统非线性刚度和非线性摩擦的谐波传动系统动力学模型；基于试验数据，采用最小二乘法对模型进行参数辨识；采用径向基函数（Radial Basis Function，RBF）神经网络在线逼近系统非线性摩擦和外界不确定干扰力矩，并提出了一种基于RBF神经网络的自适应反演控制器；利用Lyapunov稳定性理论，证明了其闭环系统的收敛性。仿真结果表明，与普通Back-stepping控制相比，在受到外界未知干扰后，所提出的RBF神经网络自适应反演控制能有效地逼近系统非线性摩擦和外界未知干扰，其跟踪误差峰-峰值能迅速稳定到0.000 82 rad；而Back-stepping控制对外界未知干扰比较敏感，其跟踪误差峰-峰值增大至0.012 3 rad左右。所提出的RBF神经网络自适应反演控制能抑制参数动态变化和外界干扰对系统传动精度的影响，提高系统的传动精度。

Abstract

Due to its own structural characteristics

a harmonic drive system has a wide range of nonlinear factors

such as flexible deformation

friction and external uncertain interference. Most of the traditional controllers simplify the system to a certain extent

or do not consider the nonlinear external disturbance

resulting in that the performance of the designed controller cannot achieve the desired results. In order to improve the accuracy of the system

the dynamic model of the harmonic drive system is established considering the nonlinear stiffness and nonlinear friction of the system. Based on the test data

the parameters of the model are identified by the least square method. Radial basis function （RBF） neural network is used to approximate the nonlinear friction and external uncertain disturbance torque of the system on-line

and an adaptive inversion controller based on RBF neural network is proposed. Using Lyapunov stability theory

the convergence of the closed-loop system is proved. The simulation results show that

compared with the ordinary Back-stepping control

the proposed RBF neural network adaptive inversion control can effectively approach the system nonlinear friction and external unknown disturbance after being subjected to external unknown disturbance

and its peak value of tracking error can be quickly stabilized to 0.000 82 rad. The Back-stepping control is sensitive to external unknown interference

and the peak value of its tracking error increases to about 0.012 3 rad. The proposed RBF neural network adaptive inversion control can suppress the influence of parameter dynamic changes and external disturbances on the transmission accuracy of the system

and improve the transmission accuracy of the system.

关键词

谐波传动系统RBF神经网络反演控制传动精度

Keywords

Harmonic drive systemRBF neural networkBack-stepping controlTransmission accuracy

references

向珍琳，李霆，杨林，等.谐波减速器研究现状及问题研究［J］.机械传动，2020，44（7）：151-162.

XIANG Zhenlin，LI Ting，YANG Lin，et al.Research status and problems of harmonic reducer［J］.Journal of Mechanical Transmission，2020，44（7）：151-162.

HU Q S，LIU Z F，CAI L G，et al.Research on prediction method of transmission accuracy of harmonic drive［C］//International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.ASME，2019，59308：V010T11A006.

HU Q S，LIU Z，YANG C B，et al.Research on dynamic transmission error of harmonic drive with uncertain parameters by an interval method［J］.Precision Engineering，2021，68：285-300.

TANG T，JIA H，LI J，et al.Modeling of transmission compliance and hysteresis considering degradation in a harmonic drive［J］.Applied Sciences，2021，11（2）：665.

邱临风，陈满意，宋港，等.基于遗传特性的谐波齿轮传动迟滞刚度模型及其参数辨识研究［J］.机械传动，2022，46（4）：37-41.

QIU Linfeng，CHEN Manyi，SONG Gang，et al.Research on hysteresis stiffness model and parameter identification of harmonic gear drive based on genetic characteristics［J］.Journal of Mechanical Transmission，2022，46（4）：37-41.

罗阳，陈满意，张杰，等.谐波齿轮传动非线性动力学建模及仿真研究［J］.机械传动，2021，45（4）：58-63.

LUO Yang，CHEN Manyi，ZHANG Jie，et al.Research on nonlinear dynamic modeling and Simulation of harmonic gear drive［J］.Journal of Mechanical Transmission，2021，45（4）：58-63.

石崟，尹华川，李俊阳，等.谐波减速器摩擦特性建模及参数辨识［J］.东北大学学报（自然科学版），2022，43（1）：89-97.

SHI Yin，YIN Huachuan，LI Junyang，et al.Friction characteristic modeling and parameter identification of harmonic reducer［J］.Journal of Northeast University（Natural Science Edition），2022，43（1）：89-97.

徐航，何元春，吴耀庭，等.机器人用精密减速器空载摩擦转矩分析与测量［J］.重庆理工大学学报（自然科学版），2020，34（11）：94-99.

XU Hang，HE Yuanchun，WU Yaoting，et al.Analysis and measurement of no-load friction torque of precision reducer for robot［J］.Journal of Chongqing University of Technology（Natural Science Edition），2020，34（11）：94-99.

KANELLAKOPOULOS I，KOKOTOVIC P V，Morse A S.Systematic design of adaptive controllers for feedback linearizable systems［C］//1991 American Control Conference.IEEE，1991，11：1241-1253.

LIU S，GANG T.Adaptive back-stepping control of the harmonic drive system with LuGre model-based friction compensation［C］//AIP Conference Proceedings.AIP Publishing LLC，2018，1944（1）：020027.

钟斌.不确定关节机器人模型的神经网络补偿自适应控制［J］.机械科学与技术，2017，36（3）：372-377.

ZHONG Bin.Neural network compensation adaptive control of uncertain joint robot model［J］.Mechanical Science and Technology for Aerospace Engineering，2017，36（3）：372-377.

沈晓斌，王斌锐，余芮，等.机器人关节摩擦建模与自适应RBF神经网络补偿计算力矩控制［J］.中国计量大学学报，2020，31（1）：71-78.

SHEN Xiaobin，WANG Binrui，YU Rui，et al.Robot joint friction modeling and adaptive RBF neural network compensation computational torque control［J］.Journal of China University of Metrology，2020，31（1）：71-78.

ABDEIRAHEEM S，HAFEZ E，SHABIB G.Optimized P-PI fuzzy logic controller of a positioning mechanism with strain wave gearing［C］//2016 Eighteenth International Middle East Power Systems Conference（MEPCON）.IEEE，2017：40-45.

刘慧博，刘尚磊.基于摩擦和干扰补偿的转台模糊反演滑模控制［J］.系统仿真学报，2018，30（3）：1195-1202.

LIU Huibo，LIU Shanglei.Fuzzy inverse sliding mode control of turntable based on friction and interference compensation［J］.Journal of System Simulation，2018，30（3）：1195-1202.

黑沫.精密谐波齿轮传动系统建模与控制方法研究［D］.长沙：国防科学技术大学，2015：63-66.

HEI MO.Research on modeling and control method of precision harmonic gear drive system［D］.Changsha：University of Defense Science and Technology，2015：63-66.

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