Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks

This paper investigates a distributed event-triggered formation tracking problem of networked three-dimensional uncertain nonlinear stratospheric airships under directed networks. It is assumed that the nonlinearities of airship followers are unknown and the leader information can be obtained by onl...

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Main Authors:	Jin Hoe Kim, Sung Jin Yoo
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Access
Subjects:	Distributed adaptive formation tracking event-triggered neural networks networked stratospheric airships
Online Access:	https://ieeexplore.ieee.org/document/9032201/

id	doaj-132e432582634f85ac6b11a97aacde1a
record_format	Article
spelling	doaj-132e432582634f85ac6b11a97aacde1a2021-03-30T01:24:41ZengIEEEIEEE Access2169-35362020-01-018499774998810.1109/ACCESS.2020.29799959032201Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural NetworksJin Hoe Kim0https://orcid.org/0000-0002-7758-5719Sung Jin Yoo1https://orcid.org/0000-0002-5580-7528School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South KoreaThis paper investigates a distributed event-triggered formation tracking problem of networked three-dimensional uncertain nonlinear stratospheric airships under directed networks. It is assumed that the nonlinearities of airship followers are unknown and the leader information can be obtained by only a subset of the airship followers. Approximation-based local adaptive tracking controllers with asynchronous event-triggering laws are developed to achieve the desired formations for both the positions and attitudes of uncertain stratospheric airship followers. We theoretically show that the stability and formation tracking performance of event-triggered closed-loop systems are ensured and Zeno behavior is excluded in the proposed asynchronous event-triggering mechanism. Finally, simulations illustrate the effectiveness of the proposed formation control protocol.https://ieeexplore.ieee.org/document/9032201/Distributed adaptive formation trackingevent-triggeredneural networksnetworked stratospheric airships
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jin Hoe Kim Sung Jin Yoo
spellingShingle	Jin Hoe Kim Sung Jin Yoo Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks IEEE Access Distributed adaptive formation tracking event-triggered neural networks networked stratospheric airships
author_facet	Jin Hoe Kim Sung Jin Yoo
author_sort	Jin Hoe Kim
title	Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks
title_short	Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks
title_full	Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks
title_fullStr	Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks
title_full_unstemmed	Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks
title_sort	distributed event-triggered adaptive formation tracking of networked uncertain stratospheric airships using neural networks
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2020-01-01
description	This paper investigates a distributed event-triggered formation tracking problem of networked three-dimensional uncertain nonlinear stratospheric airships under directed networks. It is assumed that the nonlinearities of airship followers are unknown and the leader information can be obtained by only a subset of the airship followers. Approximation-based local adaptive tracking controllers with asynchronous event-triggering laws are developed to achieve the desired formations for both the positions and attitudes of uncertain stratospheric airship followers. We theoretically show that the stability and formation tracking performance of event-triggered closed-loop systems are ensured and Zeno behavior is excluded in the proposed asynchronous event-triggering mechanism. Finally, simulations illustrate the effectiveness of the proposed formation control protocol.
topic	Distributed adaptive formation tracking event-triggered neural networks networked stratospheric airships
url	https://ieeexplore.ieee.org/document/9032201/
work_keys_str_mv	AT jinhoekim distributedeventtriggeredadaptiveformationtrackingofnetworkeduncertainstratosphericairshipsusingneuralnetworks AT sungjinyoo distributedeventtriggeredadaptiveformationtrackingofnetworkeduncertainstratosphericairshipsusingneuralnetworks
_version_	1724187100188770304

Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks

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