Identification of Robot Dynamic Parameters Based on BP Neural Network

Yingjie Zhong

doi:10.54097/er0pd855

Authors

Yingjie Zhong

DOI:

https://doi.org/10.54097/er0pd855

Keywords:

BP Neural Network, Robot Dynamics, Parameter Identification Introduction

Abstract

This paper proposes a dynamic parameter identification method based on the BP neural network to improve the accuracy of dynamic modeling and control performance of industrial robots. The method employs Fourier polynomial trajectory planning and constructs a BP neural network model for parameter identification. A six-degree-of-freedom (DOF) robotic arm is simulated using the Matlab Robotics Toolbox for validation. The results demonstrate that the proposed method can achieve accurate dynamic parameter identification under complex conditions, exhibiting robustness against environmental noise and friction disturbances. Furthermore, it enhances the efficiency and accuracy of dynamic parameter identification.

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References

[1] Robot Industry, 2022, (04):83-95. DOI:10.19609/j.cnki.cn10-1324/ tp. 2022.04.004.

[2] M. Gautier, W. Khalil. Exciting trajectories for the identification of base initial parameters ofrobots [J]. International journal or robotic research, 1992, 11 (4): 362-375.

[3] Identification of dynamic parameters of modular robot considering joint flexibility by Zhou Jun and Yu Yueqing[J]. Robot, 2011,33(04):440-448.

[4] Feng Limin, Yu Jinghu, Wang Yanyu, et al Identification of Robot Dynamic Parameters Based on IRLS Algorithm [J]. Modern Manufacturing Engineering, 2022(4):37-44.

[5] Modeling and parameter identification of leg dynamics of equirope-driven bipedal robot by Zhu Zhenbiao, Tang Jun, Liang Zhiyuan[J].Mechanical Transmission,2024,48(08):18-25.DOI:10.16578/j.issn.1004.2539.2024.08.003.

[6] Identification of dynamic parameters of Zhang Shiyuan, Dai Jun, and Deng Hua six-degree-of-freedom manipulator [J]. Manufacturing Automation,2021,43(03):35-39.

[7] Chen Zhihuan, Liang Chenglong, Dong Jiashun, Chaotic Quantum Whale Optimization Algorithm and Its Application [J]. Computer Simulation,2024,41(08):439-446+465.

[8] Liu Yu, Li Guixian, Xia Dan, et al. Identification of Dynamic Parameters of Space Robots Based on Improved Genetic Algorithm [J].Journal of Harbin Institute of Technology, 2010, 42(11):1734-1739.

[9] Minglu Zhang, Qing Wang, Xuan Liu, et al. Identification of Dynamic Parameters of Torque Compensation Based on Neural Network[J].Journal of Tianjin University (Natural Science and Engineering Technology), 2024, 57(07): 759-767.

[10] Zhang Di's trajectory tracking control of robot arm based on recurrent neural network[J].Mechanical Engineering and Automation, 2024,(04):19-21.

[11] Craig, J.JAuthors; Translated by Yi Chao et al. . Introduction to Robotics: Mechanics and Control[M]. Beijing: China Machine Press, 2006.

[12] Zhu Miao's research on the identification of dynamic parameters of SCARA robot considering joint flexibility[D]. Guangdong University of Technology,2022. DOI:10. 27029/d. cnki. ggdgu.2022.000121.

[13] Qi Changgui's contour error prediction and compensation of numerical control system based on LSTM neural network [D]. Harbin Institute of Technology,2021. DOI: 10.27061/ d.cnki. ghgdu. 2021.001067.