A Fast Learning Control Strategy for Unmanned Aerial Manipulators

We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-deriv...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of intelligent & robotic systems 2019-06, Vol.94 (3-4), p.805-824
Hauptverfasser: Imanberdiyev, Nursultan, Kayacan, Erdal
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 824
container_issue 3-4
container_start_page 805
container_title Journal of intelligent & robotic systems
container_volume 94
creator Imanberdiyev, Nursultan
Kayacan, Erdal
description We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-derivative (PD) controller working alone and the proposed intelligent control structures working in parallel with a PD controller is presented. The simulation and real-time results show that the proposed online adaptation laws eliminate the need for precise tuning of conventional controllers by learning system dynamics and disturbances online. The proposed approach is also computationally inexpensive due to the implementation of the fast sliding mode control theory-based learning algorithm which does not require matrix inversions or partial derivatives. Both simulation and experimental results have shown that the proposed artificial intelligence-based learning controller is capable of reducing the root-mean-square error by around 50% over conventional PD and PID controllers.
doi_str_mv 10.1007/s10846-018-0884-7
format Article
fullrecord <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2053206437</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A725981224</galeid><sourcerecordid>A725981224</sourcerecordid><originalsourceid>FETCH-LOGICAL-c355t-788695d0a5f1312b91dc2015021f6965e918552003f48a44e143b693b70f77903</originalsourceid><addsrcrecordid>eNp1kE9LAzEQxYMoWKsfwNuC5-jk3yY5LsWqUPGgPYd0NylbtklNtod-e1NW8CRzGBjeb97MQ-iewCMBkE-ZgOI1BqIwKMWxvEAzIiTDwEFfohloSjBQXV-jm5x3AKCV0DPUNNXS5rFaOZtCH7bVIoYxxaH6HJMd3fZU-ZiqddjbEFxXNS71dqjebegPx8GOMeVbdOXtkN3db5-j9fL5a_GKVx8vb4tmhVsmxIilUrUWHVjhCSN0o0nXUiACKPG1roXTRAlBAZjnynLuCGebWrONBC-lBjZHD9PeQ4rfR5dHs4vHFIqloSAYhZozWVSPk2prB2f64GP5oy3VuX3fxuB8X-aNpEIrQikvAJmANsWck_PmkPq9TSdDwJyjNVO0pkRrztGaswmdmFy0YevS3yn_Qz-EYHha</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2053206437</pqid></control><display><type>article</type><title>A Fast Learning Control Strategy for Unmanned Aerial Manipulators</title><source>SpringerLink Journals - AutoHoldings</source><creator>Imanberdiyev, Nursultan ; Kayacan, Erdal</creator><creatorcontrib>Imanberdiyev, Nursultan ; Kayacan, Erdal</creatorcontrib><description>We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-derivative (PD) controller working alone and the proposed intelligent control structures working in parallel with a PD controller is presented. The simulation and real-time results show that the proposed online adaptation laws eliminate the need for precise tuning of conventional controllers by learning system dynamics and disturbances online. The proposed approach is also computationally inexpensive due to the implementation of the fast sliding mode control theory-based learning algorithm which does not require matrix inversions or partial derivatives. Both simulation and experimental results have shown that the proposed artificial intelligence-based learning controller is capable of reducing the root-mean-square error by around 50% over conventional PD and PID controllers.</description><identifier>ISSN: 0921-0296</identifier><identifier>EISSN: 1573-0409</identifier><identifier>DOI: 10.1007/s10846-018-0884-7</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Artificial Intelligence ; Artificial neural networks ; Comparative analysis ; Computer simulation ; Control ; Control theory ; Controllers ; Data mining ; Derivatives (Financial instruments) ; Drone aircraft ; Electrical Engineering ; Engineering ; Fuzzy control ; Fuzzy logic ; Inversions ; Laws, regulations and rules ; Machine learning ; Manipulators ; Measuring instruments ; Mechanical Engineering ; Mechatronics ; Neural networks ; On-line systems ; Proportional integral derivative ; Robot arms ; Robotics ; Sliding mode control ; System dynamics</subject><ispartof>Journal of intelligent &amp; robotic systems, 2019-06, Vol.94 (3-4), p.805-824</ispartof><rights>Springer Science+Business Media B.V., part of Springer Nature 2018</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Journal of Intelligent &amp; Robotic Systems is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-788695d0a5f1312b91dc2015021f6965e918552003f48a44e143b693b70f77903</citedby><cites>FETCH-LOGICAL-c355t-788695d0a5f1312b91dc2015021f6965e918552003f48a44e143b693b70f77903</cites><orcidid>0000-0002-7143-8777</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10846-018-0884-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10846-018-0884-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Imanberdiyev, Nursultan</creatorcontrib><creatorcontrib>Kayacan, Erdal</creatorcontrib><title>A Fast Learning Control Strategy for Unmanned Aerial Manipulators</title><title>Journal of intelligent &amp; robotic systems</title><addtitle>J Intell Robot Syst</addtitle><description>We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-derivative (PD) controller working alone and the proposed intelligent control structures working in parallel with a PD controller is presented. The simulation and real-time results show that the proposed online adaptation laws eliminate the need for precise tuning of conventional controllers by learning system dynamics and disturbances online. The proposed approach is also computationally inexpensive due to the implementation of the fast sliding mode control theory-based learning algorithm which does not require matrix inversions or partial derivatives. Both simulation and experimental results have shown that the proposed artificial intelligence-based learning controller is capable of reducing the root-mean-square error by around 50% over conventional PD and PID controllers.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Comparative analysis</subject><subject>Computer simulation</subject><subject>Control</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Data mining</subject><subject>Derivatives (Financial instruments)</subject><subject>Drone aircraft</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Inversions</subject><subject>Laws, regulations and rules</subject><subject>Machine learning</subject><subject>Manipulators</subject><subject>Measuring instruments</subject><subject>Mechanical Engineering</subject><subject>Mechatronics</subject><subject>Neural networks</subject><subject>On-line systems</subject><subject>Proportional integral derivative</subject><subject>Robot arms</subject><subject>Robotics</subject><subject>Sliding mode control</subject><subject>System dynamics</subject><issn>0921-0296</issn><issn>1573-0409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE9LAzEQxYMoWKsfwNuC5-jk3yY5LsWqUPGgPYd0NylbtklNtod-e1NW8CRzGBjeb97MQ-iewCMBkE-ZgOI1BqIwKMWxvEAzIiTDwEFfohloSjBQXV-jm5x3AKCV0DPUNNXS5rFaOZtCH7bVIoYxxaH6HJMd3fZU-ZiqddjbEFxXNS71dqjebegPx8GOMeVbdOXtkN3db5-j9fL5a_GKVx8vb4tmhVsmxIilUrUWHVjhCSN0o0nXUiACKPG1roXTRAlBAZjnynLuCGebWrONBC-lBjZHD9PeQ4rfR5dHs4vHFIqloSAYhZozWVSPk2prB2f64GP5oy3VuX3fxuB8X-aNpEIrQikvAJmANsWck_PmkPq9TSdDwJyjNVO0pkRrztGaswmdmFy0YevS3yn_Qz-EYHha</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Imanberdiyev, Nursultan</creator><creator>Kayacan, Erdal</creator><general>Springer Netherlands</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-7143-8777</orcidid></search><sort><creationdate>20190601</creationdate><title>A Fast Learning Control Strategy for Unmanned Aerial Manipulators</title><author>Imanberdiyev, Nursultan ; Kayacan, Erdal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-788695d0a5f1312b91dc2015021f6965e918552003f48a44e143b693b70f77903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Comparative analysis</topic><topic>Computer simulation</topic><topic>Control</topic><topic>Control theory</topic><topic>Controllers</topic><topic>Data mining</topic><topic>Derivatives (Financial instruments)</topic><topic>Drone aircraft</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Inversions</topic><topic>Laws, regulations and rules</topic><topic>Machine learning</topic><topic>Manipulators</topic><topic>Measuring instruments</topic><topic>Mechanical Engineering</topic><topic>Mechatronics</topic><topic>Neural networks</topic><topic>On-line systems</topic><topic>Proportional integral derivative</topic><topic>Robot arms</topic><topic>Robotics</topic><topic>Sliding mode control</topic><topic>System dynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Imanberdiyev, Nursultan</creatorcontrib><creatorcontrib>Kayacan, Erdal</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of intelligent &amp; robotic systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Imanberdiyev, Nursultan</au><au>Kayacan, Erdal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Fast Learning Control Strategy for Unmanned Aerial Manipulators</atitle><jtitle>Journal of intelligent &amp; robotic systems</jtitle><stitle>J Intell Robot Syst</stitle><date>2019-06-01</date><risdate>2019</risdate><volume>94</volume><issue>3-4</issue><spage>805</spage><epage>824</epage><pages>805-824</pages><issn>0921-0296</issn><eissn>1573-0409</eissn><abstract>We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-derivative (PD) controller working alone and the proposed intelligent control structures working in parallel with a PD controller is presented. The simulation and real-time results show that the proposed online adaptation laws eliminate the need for precise tuning of conventional controllers by learning system dynamics and disturbances online. The proposed approach is also computationally inexpensive due to the implementation of the fast sliding mode control theory-based learning algorithm which does not require matrix inversions or partial derivatives. Both simulation and experimental results have shown that the proposed artificial intelligence-based learning controller is capable of reducing the root-mean-square error by around 50% over conventional PD and PID controllers.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10846-018-0884-7</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-7143-8777</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0921-0296
ispartof Journal of intelligent & robotic systems, 2019-06, Vol.94 (3-4), p.805-824
issn 0921-0296
1573-0409
language eng
recordid cdi_proquest_journals_2053206437
source SpringerLink Journals - AutoHoldings
subjects Algorithms
Artificial Intelligence
Artificial neural networks
Comparative analysis
Computer simulation
Control
Control theory
Controllers
Data mining
Derivatives (Financial instruments)
Drone aircraft
Electrical Engineering
Engineering
Fuzzy control
Fuzzy logic
Inversions
Laws, regulations and rules
Machine learning
Manipulators
Measuring instruments
Mechanical Engineering
Mechatronics
Neural networks
On-line systems
Proportional integral derivative
Robot arms
Robotics
Sliding mode control
System dynamics
title A Fast Learning Control Strategy for Unmanned Aerial Manipulators
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T08%3A45%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Fast%20Learning%20Control%20Strategy%20for%20Unmanned%20Aerial%20Manipulators&rft.jtitle=Journal%20of%20intelligent%20&%20robotic%20systems&rft.au=Imanberdiyev,%20Nursultan&rft.date=2019-06-01&rft.volume=94&rft.issue=3-4&rft.spage=805&rft.epage=824&rft.pages=805-824&rft.issn=0921-0296&rft.eissn=1573-0409&rft_id=info:doi/10.1007/s10846-018-0884-7&rft_dat=%3Cgale_proqu%3EA725981224%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2053206437&rft_id=info:pmid/&rft_galeid=A725981224&rfr_iscdi=true