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Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression

Dechao Chen, Shuai Li Orcid Logo, Qing Wu, Xin Luo

Neurocomputing

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.neucom.2019.08.085

Abstract

This paper considers the coordination motion control of multiple robot manipulators by developing a unified framework of super-twisting zeroing neural network (ST-ZNN), and proposes a novel external disturbances suppression model. The proposed ST-ZNN model makes new progresses of both theory and pra...

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Published in: Neurocomputing
ISSN: 0925-2312
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa52014
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first_indexed 2019-09-23T14:18:33Z
last_indexed 2019-09-26T14:20:03Z
id cronfa52014
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spelling 2019-09-26T13:15:37.9971109 v2 52014 2019-09-23 Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2019-09-23 MECH This paper considers the coordination motion control of multiple robot manipulators by developing a unified framework of super-twisting zeroing neural network (ST-ZNN), and proposes a novel external disturbances suppression model. The proposed ST-ZNN model makes new progresses of both theory and practice by overcoming two limitations in the conventional ZNN (CZNN) models, i.e., the convergence time tending to be infinitely large and the rejection of external disturbances staying at the stage of asymptotic convergence. Then, the global stability, finite-time convergence, and robustness against external disturbances are rigorously proven in the theory. Finally, illustrative coordination motion control tasks, comparisons and performance tests demonstrate the effectiveness and superiority of the proposed ST-ZNN model for coordination motion control of multiple robot manipulators. Journal Article Neurocomputing 0925-2312 Coordination motion control, Zeroing neural networks (ZNNs), Finite-time convergence, Super-twisting, Multiple robot manipulators 31 12 2019 2019-12-31 10.1016/j.neucom.2019.08.085 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2019-09-26T13:15:37.9971109 2019-09-23T11:52:34.4589807 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Dechao Chen 1 Shuai Li 0000-0001-8316-5289 2 Qing Wu 3 Xin Luo 4
title Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
spellingShingle Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
Shuai Li
title_short Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
title_full Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
title_fullStr Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
title_full_unstemmed Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
title_sort Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Shuai Li
Qing Wu
Xin Luo
format Journal article
container_title Neurocomputing
publishDate 2019
institution Swansea University
issn 0925-2312
doi_str_mv 10.1016/j.neucom.2019.08.085
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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 0
active_str 0
description This paper considers the coordination motion control of multiple robot manipulators by developing a unified framework of super-twisting zeroing neural network (ST-ZNN), and proposes a novel external disturbances suppression model. The proposed ST-ZNN model makes new progresses of both theory and practice by overcoming two limitations in the conventional ZNN (CZNN) models, i.e., the convergence time tending to be infinitely large and the rejection of external disturbances staying at the stage of asymptotic convergence. Then, the global stability, finite-time convergence, and robustness against external disturbances are rigorously proven in the theory. Finally, illustrative coordination motion control tasks, comparisons and performance tests demonstrate the effectiveness and superiority of the proposed ST-ZNN model for coordination motion control of multiple robot manipulators.
published_date 2019-12-31T04:04:08Z
_version_ 1763753327548432384
score 11.036334

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