Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments
The utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial veh...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-04-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1920.pdf |
| _version_ | 1827286433438629888 |
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| author | Zirong Wang Zhengyu Han Shahzadi Tayyaba |
| author_facet | Zirong Wang Zhengyu Han Shahzadi Tayyaba |
| author_sort | Zirong Wang |
| collection | DOAJ |
| description | The utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial vehicles (UAV) require real-time adjustments for flight safety and efficacy. To improve the attitude estimation accuracy, this article introduces a ATT-Bi-LSTM framework for optimizing UAVs through adaptive parameter control, which integrates the state information gleaned from communication signals. The ATT-Bi-LSTM achieves data feature extraction by means of a two-layer Bidirectional Long Short-Term Memory (BI-LSTM) at its inception to enhance the feature. Subsequently, it harnesses the attention mechanism to amplify the LSTM network’s output, thereby enabling the optimal control of UAV positioning. During the empirical phase, we employ optical system data for the comparative validation of the model. The outcomes underscore the commendable performance of the proposed framework in this study, particularly with regard to the three pivotal position indicators: yaw, pitch, and roll. In the comparison of indicators such as RMSR and MAE, the proposed model has the lowest error, which provides algorithm support and important reference for future UAV optimization control research. |
| first_indexed | 2024-04-24T10:32:27Z |
| format | Article |
| id | doaj.art-75957d27c362487289f88964c0f66858 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T10:32:27Z |
| publishDate | 2024-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-75957d27c362487289f88964c0f668582024-04-12T15:05:05ZengPeerJ Inc.PeerJ Computer Science2376-59922024-04-0110e192010.7717/peerj-cs.1920Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environmentsZirong Wang0Zhengyu Han1Shahzadi Tayyaba2Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an, Shaanxi, ChinaEquipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an, Shaanxi, ChinaDivision of Science and Technology, University of Education, Township Campus, University of Education, Lahore, PakistanThe utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial vehicles (UAV) require real-time adjustments for flight safety and efficacy. To improve the attitude estimation accuracy, this article introduces a ATT-Bi-LSTM framework for optimizing UAVs through adaptive parameter control, which integrates the state information gleaned from communication signals. The ATT-Bi-LSTM achieves data feature extraction by means of a two-layer Bidirectional Long Short-Term Memory (BI-LSTM) at its inception to enhance the feature. Subsequently, it harnesses the attention mechanism to amplify the LSTM network’s output, thereby enabling the optimal control of UAV positioning. During the empirical phase, we employ optical system data for the comparative validation of the model. The outcomes underscore the commendable performance of the proposed framework in this study, particularly with regard to the three pivotal position indicators: yaw, pitch, and roll. In the comparison of indicators such as RMSR and MAE, the proposed model has the lowest error, which provides algorithm support and important reference for future UAV optimization control research.https://peerj.com/articles/cs-1920.pdfLSTMBI-LSTMAttention mechanismUAV controlAttitude estimation |
| spellingShingle | Zirong Wang Zhengyu Han Shahzadi Tayyaba Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments PeerJ Computer Science LSTM BI-LSTM Attention mechanism UAV control Attitude estimation |
| title | Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments |
| title_full | Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments |
| title_fullStr | Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments |
| title_full_unstemmed | Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments |
| title_short | Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments |
| title_sort | adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments |
| topic | LSTM BI-LSTM Attention mechanism UAV control Attitude estimation |
| url | https://peerj.com/articles/cs-1920.pdf |
| work_keys_str_mv | AT zirongwang adaptivecontrolforuncrewedaerialvehiclesbasedoncommunicationinformationoptimizationincomplexenvironments AT zhengyuhan adaptivecontrolforuncrewedaerialvehiclesbasedoncommunicationinformationoptimizationincomplexenvironments AT shahzaditayyaba adaptivecontrolforuncrewedaerialvehiclesbasedoncommunicationinformationoptimizationincomplexenvironments |