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A recurrent neural network applied to optimal motion control of mobile robots with physical constraints

Dechao Chen, Shuai Li Orcid Logo, Liefa Liao

Applied Soft Computing, Volume: 85, Start page: 105880

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.asoc.2019.105880

Abstract

Conventional solutions, such as the conventional recurrent neural network (CRNN) and gradient recurrent neural network (GRNN), for the motion control of mobile robots in the unified framework of recurrent neural network (RNN) are difficult to simultaneously consider both criteria optimization and ph...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: Elsevier BV 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa52559
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spelling 2023-02-22T15:45:17.4562874 v2 52559 2019-10-24 A recurrent neural network applied to optimal motion control of mobile robots with physical constraints 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2019-10-24 MECH Conventional solutions, such as the conventional recurrent neural network (CRNN) and gradient recurrent neural network (GRNN), for the motion control of mobile robots in the unified framework of recurrent neural network (RNN) are difficult to simultaneously consider both criteria optimization and physical constraints. The limitation of the RNN solution may lead to the damage of mobile robots for exceeding physical constraints during the task execution. To overcome this limitation, this paper proposes a novel inequality and equality constrained optimization RNN (IECORNN) to handle the motion control of mobile robots. Firstly, the real-time motion control problem with both criteria optimization and physical constraints is skillfully converted to a real-time equality system by leveraging the Lagrange multiplier rule. Then, the detailed design process for the proposed IECORNN is presented together with the neural network architecture developed. Afterward, theoretical analyses on the motion control problem conversion equivalence, global stability, and exponential convergence property are rigorously provided. Finally, two numerical simulation verifications and extensive comparisons with other existing RNNs, e.g., the CRNN and the GRNN, based on the mobile robot for two different path-tracking applications sufficiently demonstrate the effectiveness and superiority of the proposed IECORNN for the real-time motion control of mobile robots with both criteria optimization and physical constraints. This work makes progresses in both theory as well as practice, and fills the vacancy in the unified framework of RNN in motion control of mobile robots. Journal Article Applied Soft Computing 85 105880 Elsevier BV 1568-4946 Recurrent neural network (RNN); Motion control; Mobile robots; Criteria optimization; Physical constraints 1 12 2019 2019-12-01 10.1016/j.asoc.2019.105880 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2023-02-22T15:45:17.4562874 2019-10-24T10:31:36.7517586 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 Liefa Liao 3 52559__15866__1bf4646b069b41b3b3a384de40c9ca2f.pdf 1IECORNN_ASC.pdf 2019-11-12T13:27:09.3791659 Output 393228 application/pdf Accepted Manuscript true 2020-10-22T00:00:00.0000000 ©2019 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
spellingShingle A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
Shuai Li
title_short A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
title_full A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
title_fullStr A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
title_full_unstemmed A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
title_sort A recurrent neural network applied to optimal motion control of mobile robots with physical constraints
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Shuai Li
Liefa Liao
format Journal article
container_title Applied Soft Computing
container_volume 85
container_start_page 105880
publishDate 2019
institution Swansea University
issn 1568-4946
doi_str_mv 10.1016/j.asoc.2019.105880
publisher Elsevier BV
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 1
active_str 0
description Conventional solutions, such as the conventional recurrent neural network (CRNN) and gradient recurrent neural network (GRNN), for the motion control of mobile robots in the unified framework of recurrent neural network (RNN) are difficult to simultaneously consider both criteria optimization and physical constraints. The limitation of the RNN solution may lead to the damage of mobile robots for exceeding physical constraints during the task execution. To overcome this limitation, this paper proposes a novel inequality and equality constrained optimization RNN (IECORNN) to handle the motion control of mobile robots. Firstly, the real-time motion control problem with both criteria optimization and physical constraints is skillfully converted to a real-time equality system by leveraging the Lagrange multiplier rule. Then, the detailed design process for the proposed IECORNN is presented together with the neural network architecture developed. Afterward, theoretical analyses on the motion control problem conversion equivalence, global stability, and exponential convergence property are rigorously provided. Finally, two numerical simulation verifications and extensive comparisons with other existing RNNs, e.g., the CRNN and the GRNN, based on the mobile robot for two different path-tracking applications sufficiently demonstrate the effectiveness and superiority of the proposed IECORNN for the real-time motion control of mobile robots with both criteria optimization and physical constraints. This work makes progresses in both theory as well as practice, and fills the vacancy in the unified framework of RNN in motion control of mobile robots.
published_date 2019-12-01T04:04:59Z
_version_ 1763753380632592384
score 11.012678

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