Real-time control based on NARX neural network of hexarotor UAV with load transporting system for path tracking

The control of equipment such as camera gimbal, Vertical Take-Off and Landing (VTOL) and Load Transporting System (LTS) on Unmanned Aerial Vehicle (UAV) with its own flight control directly affects the performance of the mission in tasks such as tracking the target along the specified path and leaving payloads on the targets specified in the dangerous areas. In this study, neural network based real-time control of a hexarotor UAV is performed so that the payloads on the targets determined by path tracking can be left with minimum error. The Nonlinear AutoRegressive eXogenous (NARX) model of the UAV is obtained after the flight data are passed through the pre-processing, feature extraction and feature selection stages. The obtained neural network model is embedded in the flight control card to realize real time path tracking of the UAV. The three payloads in the cubic structure are both transported by the originally designed LTS and left with the help of LTS to targets on the path. Environmental testing is conducted taking into account the limitations of the physical properties of the LTS and specified path tracking on the autonomously moving UAV, and the impact on proposed NARX control algorithm's mission performance is examined. © 2018 IEEE.

Dergi Adı 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018
Sayfalar -
Yayın Yılı 2018
Eser Adı
[dc.title]
Real-time control based on NARX neural network of hexarotor UAV with load transporting system for path tracking
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
conferenceObject
Açıklama
[dc.description]
6th International Conference on Control Engineering and Information Technology, CEIT 2018 -- 25 October 2018 through 27 October 2018 -- -- 149175
Özet
[dc.description.abstract]
The control of equipment such as camera gimbal, Vertical Take-Off and Landing (VTOL) and Load Transporting System (LTS) on Unmanned Aerial Vehicle (UAV) with its own flight control directly affects the performance of the mission in tasks such as tracking the target along the specified path and leaving payloads on the targets specified in the dangerous areas. In this study, neural network based real-time control of a hexarotor UAV is performed so that the payloads on the targets determined by path tracking can be left with minimum error. The Nonlinear AutoRegressive eXogenous (NARX) model of the UAV is obtained after the flight data are passed through the pre-processing, feature extraction and feature selection stages. The obtained neural network model is embedded in the flight control card to realize real time path tracking of the UAV. The three payloads in the cubic structure are both transported by the originally designed LTS and left with the help of LTS to targets on the path. Environmental testing is conducted taking into account the limitations of the physical properties of the LTS and specified path tracking on the autonomously moving UAV, and the impact on proposed NARX control algorithm's mission performance is examined. © 2018 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Yılı
[dc.date.issued]
2018
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/CEIT.2018.8751829
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/7322
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Load Transporting Sytstem (LTS)
Konu Başlıkları
[dc.subject]
Nonlinear AutoRegressive eXogenous (NARX)
Konu Başlıkları
[dc.subject]
Path tracking
Konu Başlıkları
[dc.subject]
ReliefF method
Konu Başlıkları
[dc.subject]
Unmanned Aerial Vehicles (UAV)
Yazar
[dc.contributor.author]
Altan A.
Yazar
[dc.contributor.author]
Aslan O.
Yazar
[dc.contributor.author]
Hacioglu R.
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Yazar Departmanı
[dc.contributor.department]
Zonguldak Bülent Ecevit Üniversitesi
Dergi Adı
[dc.relation.journal]
2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018
ISBN
[dc.identifier.isbn]
9781538676417
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
13
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
09 Mart 2024 07:06
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Tıklayınız
control tracking flight specified targets payloads mission network neural feature performance obtained testing algorithm embedded examined selection stages extraction pre-processing through passed realize conducted designed taking Environmental account limitations physical properties autonomously moving originally transported
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