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An efficient self-motion scheme for redundant robot manipulators: a varying-gain neural self-motion approach

Published online by Cambridge University Press:  14 May 2021

Pengchao Zhang ,
Xiaohui Ren  and
Zhijun Zhang Open the ORCID record for Zhijun Zhang [Opens in a new window]
Pengchao Zhang
Affiliation:
Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China
Xiaohui Ren
Affiliation:
Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China School of Electrical Engineering, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China
Zhijun Zhang*
Affiliation:
Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou 510335, China School of Automation Science and Engineering, East China Jiaotong University, Nanchang 330052, China
*
*Corresponding author. Email: [email protected]
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Abstract

In order to achieve high efficient self-motion for a redundant robot manipulator, a novel quadratic programming and varying-gain recurrent neural network based varying-gain neural self-motion (VGN-SM) approach is proposed and developed. With VGN-SM, the convergence errors can be adaptively and efficiently converged to zero. For comparisons, a traditional fixed-parameter neural self-motion (FPN-SM) approach is also presented. Theoretical analysis shows that the proposed VGN-SM has higher accuracy than the traditional FPN-SM. Finally, comparative experiments between VGN-SM and FPN-SM are performed on a six degrees-of-freedom robot manipulator to verify the advantages of the novel VGN-SM.

Keywords

redundant robotself-motionvarying-gain recurrent neural networkzeroing neural networkconvergence
Type
Article
Information
Robotica , Volume 39 , Issue 10 , October 2021 , pp. 1897 - 1908
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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