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Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route

Yang Shi, Wenhan Zhao, Shuai Li Orcid Logo, Bin Li, Xiaobing Sun

IEEE Transactions on Neural Networks and Learning Systems, Pages: 1 - 10

Swansea University Author: Shuai Li Orcid Logo

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Abstract

Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates...

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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/cronfa58131
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first_indexed 2021-09-28T13:46:41Z
last_indexed 2023-01-11T14:38:31Z
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spelling 2023-01-04T15:36:02.8334211 v2 58131 2021-09-28 Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-09-28 MECH Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates from the discretization research of continuous time-variant problem, and there is little research on the direct discretization method. To address the aforementioned problem, this article introduces a novel discrete-time RNN model for solving the discrete time-variant problem in a pioneering manner. Specifically, a discrete time-variant nonlinear system, which originates from the mathematical modeling of serial robot manipulator, is presented as a target problem. For solving the problem, first, the technique of second-order Taylor expansion is used to deal with the discrete time-variant nonlinear system, and the novel discrete-time RNN model is proposed subsequently. Second, the theoretical analyses are investigated and developed, which shows the convergence and precision of the proposed discrete-time RNN model. Furthermore, three distinct numerical experiments verify the excellent performance of the proposed discrete-time RNN model. In addition, a robot manipulator example further verifies the effectiveness and practicability of the proposed novel discrete-time RNN model. Journal Article IEEE Transactions on Neural Networks and Learning Systems 1 10 Institute of Electrical and Electronics Engineers (IEEE) 2162-237X 2162-2388 15 9 2021 2021-09-15 10.1109/tnnls.2021.3108050 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2023-01-04T15:36:02.8334211 2021-09-28T14:41:03.1213282 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yang Shi 1 Wenhan Zhao 2 Shuai Li 0000-0001-8316-5289 3 Bin Li 4 Xiaobing Sun 5
title Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
spellingShingle Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
Shuai Li
title_short Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
title_full Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
title_fullStr Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
title_full_unstemmed Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
title_sort Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Yang Shi
Wenhan Zhao
Shuai Li
Bin Li
Xiaobing Sun
format Journal article
container_title IEEE Transactions on Neural Networks and Learning Systems
container_start_page 1
publishDate 2021
institution Swansea University
issn 2162-237X
2162-2388
doi_str_mv 10.1109/tnnls.2021.3108050
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 Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates from the discretization research of continuous time-variant problem, and there is little research on the direct discretization method. To address the aforementioned problem, this article introduces a novel discrete-time RNN model for solving the discrete time-variant problem in a pioneering manner. Specifically, a discrete time-variant nonlinear system, which originates from the mathematical modeling of serial robot manipulator, is presented as a target problem. For solving the problem, first, the technique of second-order Taylor expansion is used to deal with the discrete time-variant nonlinear system, and the novel discrete-time RNN model is proposed subsequently. Second, the theoretical analyses are investigated and developed, which shows the convergence and precision of the proposed discrete-time RNN model. Furthermore, three distinct numerical experiments verify the excellent performance of the proposed discrete-time RNN model. In addition, a robot manipulator example further verifies the effectiveness and practicability of the proposed novel discrete-time RNN model.
published_date 2021-09-15T04:10:13Z
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score 10.927863