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Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

Chenguang Yang, Tao Teng, Bin Xu, Zhijun Li, Jing Na, Chun-Yi Su

International Journal of Control, Automation and Systems

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1007/s12555-016-0515-7

Abstract

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the...

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Published in: International Journal of Control, Automation and Systems
ISSN: 1598-6446 2005-4092
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa34729
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first_indexed 2017-07-24T05:23:53Z
last_indexed 2018-04-13T19:19:28Z
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spelling 2018-04-13T15:38:25.4642824 v2 34729 2017-07-23 Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2017-07-23 EEN In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods. Journal Article International Journal of Control, Automation and Systems 1598-6446 2005-4092 Finite-time learning convergence, globally uniformly ultimate boundedness, neural networks, robot manipulators 31 12 2017 2017-12-31 10.1007/s12555-016-0515-7 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-04-13T15:38:25.4642824 2017-07-23T23:08:01.7965725 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Chenguang Yang 1 Tao Teng 2 Bin Xu 3 Zhijun Li 4 Jing Na 5 Chun-Yi Su 6 0034729-23072017231041.pdf IJCAS17FT_plain.pdf 2017-07-23T23:10:41.2930000 Output 1064352 application/pdf Accepted Manuscript true 2018-07-20T00:00:00.0000000 false eng
title Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
spellingShingle Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
Chenguang Yang
title_short Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
title_full Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
title_fullStr Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
title_full_unstemmed Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
title_sort Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Chenguang Yang
Tao Teng
Bin Xu
Zhijun Li
Jing Na
Chun-Yi Su
format Journal article
container_title International Journal of Control, Automation and Systems
publishDate 2017
institution Swansea University
issn 1598-6446
2005-4092
doi_str_mv 10.1007/s12555-016-0515-7
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.
published_date 2017-12-31T03:43:05Z
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score 11.036706

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