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Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

Linghuan Kong Orcid Logo, Wei He Orcid Logo, Chenguang Yang, Zhijun Li Orcid Logo, Changyin Sun Orcid Logo

IEEE Transactions on Cybernetics, Volume: 49, Issue: 8, Pages: 3052 - 3063

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1109/tcyb.2018.2838573

Abstract

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning a...

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Published in: IEEE Transactions on Cybernetics
ISSN: 2168-2267 2168-2275
Published: Institute of Electrical and Electronics Engineers (IEEE) 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa50486
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last_indexed 2019-07-12T15:41:07Z
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spelling v2 50486 2019-05-22 Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2019-05-22 EEN In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment–robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov’s stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control. Journal Article IEEE Transactions on Cybernetics 49 8 3052 3063 Institute of Electrical and Electronics Engineers (IEEE) 2168-2267 2168-2275 1 8 2019 2019-08-01 10.1109/tcyb.2018.2838573 http://dx.doi.org/10.1109/tcyb.2018.2838573 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2023-06-02T15:19:00.6278193 2019-05-22T13:48:25.3684530 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Linghuan Kong 0000-0001-9866-4822 1 Wei He 0000-0002-8944-9861 2 Chenguang Yang 3 Zhijun Li 0000-0002-3909-488x 4 Changyin Sun 0000-0001-9269-334x 5 0050486-12072019084946.pdf kong2019.pdf 2019-07-12T08:49:46.7070000 Output 7874427 application/pdf Accepted Manuscript true 2019-07-12T00:00:00.0000000 false eng
title Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
spellingShingle Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
Chenguang Yang
title_short Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
title_full Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
title_fullStr Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
title_full_unstemmed Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
title_sort Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Linghuan Kong
Wei He
Chenguang Yang
Zhijun Li
Changyin Sun
format Journal article
container_title IEEE Transactions on Cybernetics
container_volume 49
container_issue 8
container_start_page 3052
publishDate 2019
institution Swansea University
issn 2168-2267
2168-2275
doi_str_mv 10.1109/tcyb.2018.2838573
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 Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
url http://dx.doi.org/10.1109/tcyb.2018.2838573
document_store_str 1
active_str 0
description In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment–robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov’s stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control.
published_date 2019-08-01T15:18:59Z
_version_ 1767600857223790592
score 11.013194

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