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...
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Veröffentlicht in: | Journal of intelligent & robotic systems 2019-06, Vol.94 (3-4), p.805-824 |
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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 |
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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 & 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 & 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 & 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 & Communications Abstracts</collection><collection>Mechanical & 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 & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & 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 & Aerospace Database</collection><collection>ProQuest Advanced Technologies & 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 & 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 & 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. 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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 |
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