UAV Formation Algorithm Based on Azimuthal Adaptive Distributed Control

Aiming at the problem of efficient, accurate and stable control of UAV formation in complex environment, the azimuthal adaptive distributed control algorithm (AADCA) is proposed, and distributed adaptive control is realized by swarm intelligence. The relative positions between UAVs are obtained through predefined formation information and azimuth-based solving, and each UAV adjusts its own position independently, iteratively processes the data, and converges in a distributed manner to maintain itself at the specified position. Simulation experiments show that the algorithm can effectively maintain the UAV formation in the case of communication silence, and improve the anti-interference ability in the complex electromagnetic environment. On the basis of maintaining high robustness, it effectively reduces the computational complexity, further reduces the occupation of UAV resources and has high dynamic stability. It is beneficial to the efficient and accurate adaptive control of UAV formation and promotes the further development of UAV industry.

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Publication Details

Published
2025-06-20
DOI
https://doi.org/10.1109/icetac65964.2025.11144138
Citations
1
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.49

Funders

Controls
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article

UAV Formation Algorithm Based on Azimuthal Adaptive Distributed Control

Xiangjun Cheng, Xiaoyu Song, Wenqian Zhang, Zongxing Zhao
1 citations
Distributed Control Multi-Agent Systems
1.49
article

UAV Formation Algorithm Based on Azimuthal Adaptive Distributed Control

Xiangjun Cheng, Xiaoyu Song, Wenqian Zhang, Zongxing Zhao
article en
1 citations

Abstract

Aiming at the problem of efficient, accurate and stable control of UAV formation in complex environment, the azimuthal adaptive distributed control algorithm (AADCA) is proposed, and distributed adaptive control is realized by swarm intelligence. The relative positions between UAVs are obtained through predefined formation information and azimuth-based solving, and each UAV adjusts its own position independently, iteratively processes the data, and converges in a distributed manner to maintain itself at the specified position. Simulation experiments show that the algorithm can effectively maintain the UAV formation in the case of communication silence, and improve the anti-interference ability in the complex electromagnetic environment. On the basis of maintaining high robustness, it effectively reduces the computational complexity, further reduces the occupation of UAV resources and has high dynamic stability. It is beneficial to the efficient and accurate adaptive control of UAV formation and promotes the further development of UAV industry.

Shuozhou Central Hospital (CN), Air Force Engineering University (CN), Av Engineering (Czechia) (CZ)
China Postdoctoral Science Foundation
Openalex Percentile: Top 13%
Distributed Control Multi-Agent Systems
1.49
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UAV Formation Algorithm Based on Azimuthal Adaptive Distributed Control — Xiangjun Cheng, Xiaoyu Song, et al. · (2025) | TGRS Research Map | TGRS