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A Systematic Error Compensation Strategy Based on an Optimized Recurrent Neural Network for Collaborative Robot Dynamics

1
Guangzhou Institute of Advanced Technology, Chinese Academy of Science, Guangzhou 511458, China
2
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
3
Department of Robot Engineering, ERICA Campus, Hanyang University, Gyeonggi-do 426-791, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6743; https://doi.org/10.3390/app10196743
Received: 10 August 2020 / Revised: 18 September 2020 / Accepted: 22 September 2020 / Published: 26 September 2020
(This article belongs to the Section Robotics and Automation)
Robot dynamics and its parameter identification are of great significance to the realization of optimal control and human–machine interaction. The objective of this research is to address the shortcomings of establishing and identifying the self-developed six-degree-of-freedom (6-DoF) collaborative robot dynamics, which leads to a large error in the predicted torque of the proposed robot. A long short-term memory (LSTM) in an optimized recurrent neural network (RNN) is proposed to compensate the dynamic model of the proposed 6-DoF collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. The analysis and experimental findings provide promising results. The compensated collaborative robot dynamic model based on LSTM in an optimized RNN displays a good prediction on the actual torque, and the root-mean-square (RMS) error between predicted and actual torques are reduced by 61.8% to 78.9% compared to the traditional dynamic model. Results of the experimental applications demonstrate the validity of the proposed systematic error compensation strategy. View Full-Text
Keywords: collaborative robot; manipulator dynamics; error compensation; recurrent neural networks; long short-term memory collaborative robot; manipulator dynamics; error compensation; recurrent neural networks; long short-term memory
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MDPI and ACS Style

Zhang, G.; Xu, Z.; Hou, Z.; Yang, W.; Liang, J.; Yang, G.; Wang, J.; Wang, H.; Han, C. A Systematic Error Compensation Strategy Based on an Optimized Recurrent Neural Network for Collaborative Robot Dynamics. Appl. Sci. 2020, 10, 6743.

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