Loading…
Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters
Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative...
Saved in:
Published in: | Neural computing & applications 2024, Vol.36 (1), p.413-424 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c270t-12801a8ac8c27406bf08aaadeca6921a4fae3e9c771eac62cde7d7785ff2acbb3 |
container_end_page | 424 |
container_issue | 1 |
container_start_page | 413 |
container_title | Neural computing & applications |
container_volume | 36 |
creator | Alqudsi, Yunes Sh Saleh, Radhwan A. A. Makaraci, Murat Ertunç, H. Metin |
description | Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at
https://youtu.be/aJMq8ROW51g
. |
doi_str_mv | 10.1007/s00521-023-09014-w |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2910037764</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2910037764</sourcerecordid><originalsourceid>FETCH-LOGICAL-c270t-12801a8ac8c27406bf08aaadeca6921a4fae3e9c771eac62cde7d7785ff2acbb3</originalsourceid><addsrcrecordid>eNp9kF1L7DAQhoMcwT2rf8CrgNfVSdJt2ksRv0A4N8frME3TbWS3qZMUWX-9WVfxzqtheJ93Bh7GzgVcCgB9FQFWUhQgVQENiLJ4O2ILUSpVKFjVf9gCmjLHValO2N8YXwCgrOrVgu1uxwFH68c1R0ceN5xCG1Lkk6M-0DZnjqeBwrwe9tEcEx92LfmO2zAmChuOY8e3LuHgZvIxecvDlPzWv2PyYeSh_yY3jviEhBl2FE_ZcY-b6M6-5pI9393-v3konv7dP95cPxVWakiFkDUIrNHWeS-hanuoEbFzFqtGCix7dMo1Vmvh0FbSdk53Wtervpdo21Yt2cXh7kThdXYxmZcw05hfGtlkeUrrrGXJ5IGyFGIk15uJ_BZpZwSYvWJzUGyyYvOp2LzlkjqUYobHtaOf07-0PgBrJoQh</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2910037764</pqid></control><display><type>article</type><title>Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters</title><source>Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List</source><creator>Alqudsi, Yunes Sh ; Saleh, Radhwan A. A. ; Makaraci, Murat ; Ertunç, H. Metin</creator><creatorcontrib>Alqudsi, Yunes Sh ; Saleh, Radhwan A. A. ; Makaraci, Murat ; Ertunç, H. Metin</creatorcontrib><description>Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at
https://youtu.be/aJMq8ROW51g
.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-023-09014-w</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Algorithms ; Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Controllers ; Data Mining and Knowledge Discovery ; Heuristic methods ; Hybrid control ; Image Processing and Computer Vision ; Optimization ; Original Article ; Parameters ; Performance evaluation ; Probability and Statistics in Computer Science ; Rescue operations ; Robots ; Robust control ; Search and rescue missions ; Swarm intelligence ; Tracking errors ; Trajectories ; Trigonometric functions</subject><ispartof>Neural computing & applications, 2024, Vol.36 (1), p.413-424</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-12801a8ac8c27406bf08aaadeca6921a4fae3e9c771eac62cde7d7785ff2acbb3</cites><orcidid>0000-0001-9945-3672</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids></links><search><creatorcontrib>Alqudsi, Yunes Sh</creatorcontrib><creatorcontrib>Saleh, Radhwan A. A.</creatorcontrib><creatorcontrib>Makaraci, Murat</creatorcontrib><creatorcontrib>Ertunç, H. Metin</creatorcontrib><title>Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at
https://youtu.be/aJMq8ROW51g
.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Controllers</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Heuristic methods</subject><subject>Hybrid control</subject><subject>Image Processing and Computer Vision</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Parameters</subject><subject>Performance evaluation</subject><subject>Probability and Statistics in Computer Science</subject><subject>Rescue operations</subject><subject>Robots</subject><subject>Robust control</subject><subject>Search and rescue missions</subject><subject>Swarm intelligence</subject><subject>Tracking errors</subject><subject>Trajectories</subject><subject>Trigonometric functions</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kF1L7DAQhoMcwT2rf8CrgNfVSdJt2ksRv0A4N8frME3TbWS3qZMUWX-9WVfxzqtheJ93Bh7GzgVcCgB9FQFWUhQgVQENiLJ4O2ILUSpVKFjVf9gCmjLHValO2N8YXwCgrOrVgu1uxwFH68c1R0ceN5xCG1Lkk6M-0DZnjqeBwrwe9tEcEx92LfmO2zAmChuOY8e3LuHgZvIxecvDlPzWv2PyYeSh_yY3jviEhBl2FE_ZcY-b6M6-5pI9393-v3konv7dP95cPxVWakiFkDUIrNHWeS-hanuoEbFzFqtGCix7dMo1Vmvh0FbSdk53Wtervpdo21Yt2cXh7kThdXYxmZcw05hfGtlkeUrrrGXJ5IGyFGIk15uJ_BZpZwSYvWJzUGyyYvOp2LzlkjqUYobHtaOf07-0PgBrJoQh</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Alqudsi, Yunes Sh</creator><creator>Saleh, Radhwan A. A.</creator><creator>Makaraci, Murat</creator><creator>Ertunç, H. Metin</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0001-9945-3672</orcidid></search><sort><creationdate>2024</creationdate><title>Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters</title><author>Alqudsi, Yunes Sh ; Saleh, Radhwan A. A. ; Makaraci, Murat ; Ertunç, H. Metin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-12801a8ac8c27406bf08aaadeca6921a4fae3e9c771eac62cde7d7785ff2acbb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Controllers</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Heuristic methods</topic><topic>Hybrid control</topic><topic>Image Processing and Computer Vision</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Parameters</topic><topic>Performance evaluation</topic><topic>Probability and Statistics in Computer Science</topic><topic>Rescue operations</topic><topic>Robots</topic><topic>Robust control</topic><topic>Search and rescue missions</topic><topic>Swarm intelligence</topic><topic>Tracking errors</topic><topic>Trajectories</topic><topic>Trigonometric functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alqudsi, Yunes Sh</creatorcontrib><creatorcontrib>Saleh, Radhwan A. A.</creatorcontrib><creatorcontrib>Makaraci, Murat</creatorcontrib><creatorcontrib>Ertunç, H. Metin</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</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><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alqudsi, Yunes Sh</au><au>Saleh, Radhwan A. A.</au><au>Makaraci, Murat</au><au>Ertunç, H. Metin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2024</date><risdate>2024</risdate><volume>36</volume><issue>1</issue><spage>413</spage><epage>424</epage><pages>413-424</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at
https://youtu.be/aJMq8ROW51g
.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-023-09014-w</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-9945-3672</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0941-0643 |
ispartof | Neural computing & applications, 2024, Vol.36 (1), p.413-424 |
issn | 0941-0643 1433-3058 |
language | eng |
recordid | cdi_proquest_journals_2910037764 |
source | Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
subjects | Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Controllers Data Mining and Knowledge Discovery Heuristic methods Hybrid control Image Processing and Computer Vision Optimization Original Article Parameters Performance evaluation Probability and Statistics in Computer Science Rescue operations Robots Robust control Search and rescue missions Swarm intelligence Tracking errors Trajectories Trigonometric functions |
title | Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T10%3A22%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Enhancing%20aerial%20robots%20performance%20through%20robust%20hybrid%20control%20and%20metaheuristic%20optimization%20of%20controller%20parameters&rft.jtitle=Neural%20computing%20&%20applications&rft.au=Alqudsi,%20Yunes%20Sh&rft.date=2024&rft.volume=36&rft.issue=1&rft.spage=413&rft.epage=424&rft.pages=413-424&rft.issn=0941-0643&rft.eissn=1433-3058&rft_id=info:doi/10.1007/s00521-023-09014-w&rft_dat=%3Cproquest_cross%3E2910037764%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c270t-12801a8ac8c27406bf08aaadeca6921a4fae3e9c771eac62cde7d7785ff2acbb3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2910037764&rft_id=info:pmid/&rfr_iscdi=true |