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Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments

Alex M. C. Smith, Chenguang Yang, Hongbin Ma, Phil Culverhouse, Angelo Cangelosi, Etienne Burdet

PLOS ONE, Volume: 10, Issue: 6, Start page: e0129281

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1371/journal.pone.0129281

Abstract

In this paper we present a hybrid control scheme, combining the advantages of task-spaceand joint-space control. The controller is based on a human-like adaptive design, which minimisesboth control effort and tracking error. Our novel hybrid adaptive controller has beentested in extensive simulation...

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Published in: PLOS ONE
ISSN: 1932-6203
Published: 2015
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URI: https://cronfa.swan.ac.uk/Record/cronfa27020
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last_indexed 2018-04-13T19:03:50Z
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spelling 2018-04-13T15:33:18.6225509 v2 27020 2016-04-01 Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-04-01 In this paper we present a hybrid control scheme, combining the advantages of task-spaceand joint-space control. The controller is based on a human-like adaptive design, which minimisesboth control effort and tracking error. Our novel hybrid adaptive controller has beentested in extensive simulations, in a scenario where a Baxter robot manipulator is affectedby external disturbances in the form of interaction with the environment and tool-like end-effectorperturbations. The results demonstrated improved performance in the hybrid controllerover both of its component parts. In addition, we introduce a novel method for onlineadaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledgefrom the experimenter. This mechanism of meta-learning induces further improvementin performance and avoids the need for tuning through trial testing. Journal Article PLOS ONE 10 6 e0129281 1932-6203 1 6 2015 2015-06-01 10.1371/journal.pone.0129281 COLLEGE NANME COLLEGE CODE Swansea University RCUK 2018-04-13T15:33:18.6225509 2016-04-01T16:11:21.0693432 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Alex M. C. Smith 1 Chenguang Yang 2 Hongbin Ma 3 Phil Culverhouse 4 Angelo Cangelosi 5 Etienne Burdet 6 0027020-01042016163039.pdf PLOS15hybrid.pdf 2016-04-01T16:30:39.3670000 Output 2975048 application/pdf Version of Record true 2016-05-17T00:00:00.0000000 Open access under creative commons attribution license. true
title Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
spellingShingle Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
Chenguang Yang
title_short Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
title_full Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
title_fullStr Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
title_full_unstemmed Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
title_sort Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Alex M. C. Smith
Chenguang Yang
Hongbin Ma
Phil Culverhouse
Angelo Cangelosi
Etienne Burdet
format Journal article
container_title PLOS ONE
container_volume 10
container_issue 6
container_start_page e0129281
publishDate 2015
institution Swansea University
issn 1932-6203
doi_str_mv 10.1371/journal.pone.0129281
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
document_store_str 1
active_str 0
description In this paper we present a hybrid control scheme, combining the advantages of task-spaceand joint-space control. The controller is based on a human-like adaptive design, which minimisesboth control effort and tracking error. Our novel hybrid adaptive controller has beentested in extensive simulations, in a scenario where a Baxter robot manipulator is affectedby external disturbances in the form of interaction with the environment and tool-like end-effectorperturbations. The results demonstrated improved performance in the hybrid controllerover both of its component parts. In addition, we introduce a novel method for onlineadaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledgefrom the experimenter. This mechanism of meta-learning induces further improvementin performance and avoids the need for tuning through trial testing.
published_date 2015-06-01T06:54:07Z
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score 11.048149

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