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Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications

Ashraf Fahmy Abdo Orcid Logo, A.M. Abdel Ghany

Ain Shams Engineering Journal, Volume: 4, Issue: 4, Pages: 805 - 829

Swansea University Author: Ashraf Fahmy Abdo Orcid Logo

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DOI (Published version): 10.1016/j.asej.2013.02.010

Abstract

This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is...

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Published in: Ain Shams Engineering Journal
ISSN: 2090-4479
Published: Elsevier BV 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa62566
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first_indexed 2023-02-27T16:24:27Z
last_indexed 2023-02-28T04:20:29Z
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spelling 2023-02-27T16:25:38.0128910 v2 62566 2023-02-04 Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications b952b837f8a8447055210d209892b427 0000-0003-1624-1725 Ashraf Fahmy Abdo Ashraf Fahmy Abdo true false 2023-02-04 MECH This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller. Journal Article Ain Shams Engineering Journal 4 4 805 829 Elsevier BV 2090-4479 Dynamic systems; Fuzzy systems; Fuzzy-PID controllers; Neuro-fuzzy systems; Robot manipulators 1 12 2013 2013-12-01 10.1016/j.asej.2013.02.010 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2023-02-27T16:25:38.0128910 2023-02-04T03:38:37.0836762 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ashraf Fahmy Abdo 0000-0003-1624-1725 1 A.M. Abdel Ghany 2
title Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
spellingShingle Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
Ashraf Fahmy Abdo
title_short Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
title_full Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
title_fullStr Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
title_full_unstemmed Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
title_sort Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications
author_id_str_mv b952b837f8a8447055210d209892b427
author_id_fullname_str_mv b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
author Ashraf Fahmy Abdo
author2 Ashraf Fahmy Abdo
A.M. Abdel Ghany
format Journal article
container_title Ain Shams Engineering Journal
container_volume 4
container_issue 4
container_start_page 805
publishDate 2013
institution Swansea University
issn 2090-4479
doi_str_mv 10.1016/j.asej.2013.02.010
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 0
active_str 0
description This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.
published_date 2013-12-01T04:22:17Z
_version_ 1763754468491395072
score 11.013596

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