scholarly journals Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 138102-138116
Author(s):  
Shoujun Wang ◽  
Xingmao Shao ◽  
Liusong Yang ◽  
Nan Liu
Keyword(s):  
Deep Learning ◽  
Parameter Identification ◽  
Robot Manipulators ◽  
Dynamic Parameter ◽  
Dynamic Parameter Identification

scholarly journals Closed-Loop Dynamic Parameter Identification of Robot Manipulators Using Modified Fourier Series

10.5772/45818 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Wenxiang Wu ◽  
Shiqiang Zhu ◽  
Xuanyin Wang ◽  
Huashan Liu
Keyword(s):  
Fourier Series ◽  
Parameter Identification ◽  
Closed Loop ◽  
Robot Manipulators ◽  
Dynamic Parameter ◽  
Measurement Noise ◽  
Identification Accuracy ◽  
Parameter Estimates ◽  
Identification Procedure ◽  
Dynamic Parameter Identification

This paper concerns the problem of dynamic parameter identification of robot manipulators and proposes a closed-loop identification procedure using modified Fourier series (MFS) as exciting trajectories. First, a static continuous friction model is involved to model joint friction for realizable friction compensation in controller design. Second, MFS satisfying the boundary conditions are firstly designed as periodic exciting trajectories. To minimize the sensitivity to measurement noise, the coefficients of MFS are optimized according to the condition number criterion. Moreover, to obtain accurate parameter estimates, the maximum likelihood estimation (MLE) method considering the influence of measurement noise is adopted. The proposed identification procedure has been implemented on the first three axes of the QIANJIANG-I 6-DOF robot manipulator. Experiment results verify the effectiveness of the proposed approach, and comparison between identification using MFS and that using finite Fourier series (FFS) reveals that the proposed method achieves better identification accuracy.

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