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A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators

Dechao Chen, Shuai Li Orcid Logo, Qing Wu

IEEE Transactions on Neural Networks and Learning Systems, Volume: 32, Issue: 4, Pages: 1776 - 1787

Swansea University Author: Shuai Li Orcid Logo

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Abstract

Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to b...

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Published in: IEEE Transactions on Neural Networks and Learning Systems
ISSN: 2162-237X 2162-2388
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56719
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first_indexed 2021-04-22T08:02:17Z
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spelling 2021-05-18T13:28:23.1184629 v2 56719 2021-04-22 A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-04-22 MECH Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to be infinitely large. The second one is the research of robustness that remains in the analyses of stability and asymptotic convergence. To simultaneously enhance the convergence performance and robustness, this article proposes a new ZNN model by using a supertwisting (ST) algorithm, termed STZNN model, for the tracking control of mobile robot manipulators. The proposed STZNN model inherently possesses the advantages of finite-time convergence and robustness making the control process fast and robust. The bridge from the sliding mode control to the ZNN is built, and the essential connection between the ST algorithm and ZNN is explored by constructing a unified design process. Theorems and proofs about global stability, finite-time convergence, and robustness are provided. Finally, path-tracking applications, comparisons, and tests substantiate the effectiveness and superiority of the STZNN model for the tracking control handling of mobile robot manipulators. Journal Article IEEE Transactions on Neural Networks and Learning Systems 32 4 1776 1787 Institute of Electrical and Electronics Engineers (IEEE) 2162-237X 2162-2388 1 4 2021 2021-04-01 10.1109/tnnls.2020.2991088 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-05-18T13:28:23.1184629 2021-04-22T09:00:41.1682174 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
title A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
spellingShingle A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
Shuai Li
title_short A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
title_full A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
title_fullStr A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
title_full_unstemmed A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
title_sort A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Shuai Li
Qing Wu
format Journal article
container_title IEEE Transactions on Neural Networks and Learning Systems
container_volume 32
container_issue 4
container_start_page 1776
publishDate 2021
institution Swansea University
issn 2162-237X
2162-2388
doi_str_mv 10.1109/tnnls.2020.2991088
publisher Institute of Electrical and Electronics Engineers (IEEE)
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 Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to be infinitely large. The second one is the research of robustness that remains in the analyses of stability and asymptotic convergence. To simultaneously enhance the convergence performance and robustness, this article proposes a new ZNN model by using a supertwisting (ST) algorithm, termed STZNN model, for the tracking control of mobile robot manipulators. The proposed STZNN model inherently possesses the advantages of finite-time convergence and robustness making the control process fast and robust. The bridge from the sliding mode control to the ZNN is built, and the essential connection between the ST algorithm and ZNN is explored by constructing a unified design process. Theorems and proofs about global stability, finite-time convergence, and robustness are provided. Finally, path-tracking applications, comparisons, and tests substantiate the effectiveness and superiority of the STZNN model for the tracking control handling of mobile robot manipulators.
published_date 2021-04-01T04:07:20Z
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score 10.928009