Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements

This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajec...
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Autor*in:	Tengfei Zhang [verfasserIn] Yingmin Jia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking

Übergeordnetes Werk:	In: Journal of Robotics, Networking and Artificial Life (JRNAL) - Atlantis Press, 2017, 7(2020), 1
Übergeordnetes Werk:	volume:7 ; year:2020 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2991/jrnal.k.200512.007

Katalog-ID:	DOAJ045380481

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allfields	10.2991/jrnal.k.200512.007 doi (DE-627)DOAJ045380481 (DE-599)DOAJd426a123b6ac4fbf92e6219d958071e7 DE-627 ger DE-627 rakwb eng Tengfei Zhang verfasserin aut Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme. Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 7(2020), 1 (DE-627)1005552487 23526386 nnns volume:7 year:2020 number:1 https://doi.org/10.2991/jrnal.k.200512.007 kostenfrei https://doaj.org/article/d426a123b6ac4fbf92e6219d958071e7 kostenfrei https://www.atlantis-press.com/article/125940592/view kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1
spelling	10.2991/jrnal.k.200512.007 doi (DE-627)DOAJ045380481 (DE-599)DOAJd426a123b6ac4fbf92e6219d958071e7 DE-627 ger DE-627 rakwb eng Tengfei Zhang verfasserin aut Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme. Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 7(2020), 1 (DE-627)1005552487 23526386 nnns volume:7 year:2020 number:1 https://doi.org/10.2991/jrnal.k.200512.007 kostenfrei https://doaj.org/article/d426a123b6ac4fbf92e6219d958071e7 kostenfrei https://www.atlantis-press.com/article/125940592/view kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1
allfields_unstemmed	10.2991/jrnal.k.200512.007 doi (DE-627)DOAJ045380481 (DE-599)DOAJd426a123b6ac4fbf92e6219d958071e7 DE-627 ger DE-627 rakwb eng Tengfei Zhang verfasserin aut Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme. Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 7(2020), 1 (DE-627)1005552487 23526386 nnns volume:7 year:2020 number:1 https://doi.org/10.2991/jrnal.k.200512.007 kostenfrei https://doaj.org/article/d426a123b6ac4fbf92e6219d958071e7 kostenfrei https://www.atlantis-press.com/article/125940592/view kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1
allfieldsGer	10.2991/jrnal.k.200512.007 doi (DE-627)DOAJ045380481 (DE-599)DOAJd426a123b6ac4fbf92e6219d958071e7 DE-627 ger DE-627 rakwb eng Tengfei Zhang verfasserin aut Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme. Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 7(2020), 1 (DE-627)1005552487 23526386 nnns volume:7 year:2020 number:1 https://doi.org/10.2991/jrnal.k.200512.007 kostenfrei https://doaj.org/article/d426a123b6ac4fbf92e6219d958071e7 kostenfrei https://www.atlantis-press.com/article/125940592/view kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1
allfieldsSound	10.2991/jrnal.k.200512.007 doi (DE-627)DOAJ045380481 (DE-599)DOAJd426a123b6ac4fbf92e6219d958071e7 DE-627 ger DE-627 rakwb eng Tengfei Zhang verfasserin aut Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme. Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 7(2020), 1 (DE-627)1005552487 23526386 nnns volume:7 year:2020 number:1 https://doi.org/10.2991/jrnal.k.200512.007 kostenfrei https://doaj.org/article/d426a123b6ac4fbf92e6219d958071e7 kostenfrei https://www.atlantis-press.com/article/125940592/view kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1
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topic_facet	Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking Technology T
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author	Tengfei Zhang
spellingShingle	Tengfei Zhang misc Strict-feedback multi-agent systems misc rotating encirclement control misc target localization misc trajectory planning misc trajectory tracking misc Technology misc T Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements
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topic_title	Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements Strict-feedback multi-agent systems rotating encirclement control target localization trajectory planning trajectory tracking
topic	misc Strict-feedback multi-agent systems misc rotating encirclement control misc target localization misc trajectory planning misc trajectory tracking misc Technology misc T
topic_unstemmed	misc Strict-feedback multi-agent systems misc rotating encirclement control misc target localization misc trajectory planning misc trajectory tracking misc Technology misc T
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title	Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements
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title_full	Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements
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title_sort	distributed rotating encirclement control of strict-feedback multi-agent systems using bearing measurements
title_auth	Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements
abstract	This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme.
abstractGer	This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme.
abstract_unstemmed	This paper focuses on the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using bearing measurements. To this end, an estimator is presented to localize agents’ neighbor targets. Then, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are utilized to obtain the targets’ geometric center, the reference rotating radius and angular. Finally, an adaptive neural dynamic surface control scheme is proposed to drive all agents to move along their reference trajectories, which satisfy the multi-target rotating encirclement formation conditions. A numerical simulation is provided to verify the correctness and effectiveness of our proposed control scheme.
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title_short	Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems using Bearing Measurements
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