Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
Identifieur interne : 000700 ( Main/Exploration ); précédent : 000699; suivant : 000701Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments
Auteurs : Alex M. C. Smith [Royaume-Uni] ; Chenguang Yang [Royaume-Uni, République populaire de Chine] ; Hongbin Ma [République populaire de Chine] ; Phil Culverhouse [Royaume-Uni] ; Angelo Cangelosi [Royaume-Uni] ; Etienne Burdet [Royaume-Uni]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.
Url:
DOI: 10.1371/journal.pone.0129281
PubMed: 26029916
PubMed Central: 4452518
Affiliations:
- Royaume-Uni, République populaire de Chine
- Angleterre, Grand Londres, Guangdong
- Jiangmen, Londres, Pékin
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Le document en format XML
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Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments</title><author><name sortKey="Smith, Alex M C" sort="Smith, Alex M C" uniqKey="Smith A" first="Alex M. C." last="Smith">Alex M. C. Smith</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Centre for Robotics and Neural Systems, Plymouth University, Plymouth</wicri:regionArea><wicri:noRegion>Plymouth</wicri:noRegion></affiliation></author><author><name sortKey="Yang, Chenguang" sort="Yang, Chenguang" uniqKey="Yang C" first="Chenguang" last="Yang">Chenguang Yang</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Centre for Robotics and Neural Systems, Plymouth University, Plymouth</wicri:regionArea><wicri:noRegion>Plymouth</wicri:noRegion></affiliation><affiliation wicri:level="3"><nlm:aff id="aff002"><addr-line>College of Automation Science and Engineering, South China University of Technology, Guangzhou, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Automation Science and Engineering, South China University of Technology, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Ma, Hongbin" sort="Ma, Hongbin" uniqKey="Ma H" first="Hongbin" last="Ma">Hongbin Ma</name><affiliation wicri:level="3"><nlm:aff id="aff003"><addr-line>State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:aff id="aff004"><addr-line>School of Automation, Beijing Institute of Technology, Beijing, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Automation, Beijing Institute of Technology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Culverhouse, Phil" sort="Culverhouse, Phil" uniqKey="Culverhouse P" first="Phil" last="Culverhouse">Phil Culverhouse</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Centre for Robotics and Neural Systems, Plymouth University, Plymouth</wicri:regionArea><wicri:noRegion>Plymouth</wicri:noRegion></affiliation></author><author><name sortKey="Cangelosi, Angelo" sort="Cangelosi, Angelo" uniqKey="Cangelosi A" first="Angelo" last="Cangelosi">Angelo Cangelosi</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Centre for Robotics and Neural Systems, Plymouth University, Plymouth</wicri:regionArea><wicri:noRegion>Plymouth</wicri:noRegion></affiliation></author><author><name sortKey="Burdet, Etienne" sort="Burdet, Etienne" uniqKey="Burdet E" first="Etienne" last="Burdet">Etienne Burdet</name><affiliation wicri:level="3"><nlm:aff id="aff005"><addr-line>Department of Bioengineering, Imperial College London, London, UK</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Bioengineering, Imperial College London, London</wicri:regionArea><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. 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This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Peshkin, Ma" uniqKey="Peshkin M">MA Peshkin</name></author><author><name sortKey="Colgate, Je" uniqKey="Colgate J">JE Colgate</name></author><author><name sortKey="Wannasuphoprasit, W" uniqKey="Wannasuphoprasit W">W Wannasuphoprasit</name></author><author><name sortKey="Moore, Ca" uniqKey="Moore C">CA Moore</name></author><author><name sortKey="Gillespie, Rb" uniqKey="Gillespie R">RB Gillespie</name></author><author><name sortKey="Akella, P" uniqKey="Akella P">P Akella</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lambercy, O" uniqKey="Lambercy O">O Lambercy</name></author><author><name sortKey="Dovat, L" uniqKey="Dovat L">L Dovat</name></author><author><name sortKey="Gassert, R" uniqKey="Gassert R">R 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