Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus

Abstract Formation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter‐UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus‐based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter‐UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

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Publication Details

Journal
IET Control Theory and Applications
Published
2024-11-28
DOI
https://doi.org/10.1049/cth2.12772
Citations
6
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
2.09
Controls
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article

Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus

Yanhong Shi, Hongrui Chen, Yuhang Zhang, Yunming Wang et al.
6 citations
IET Control Theory and Applications
Distributed Control Multi-Agent Systems
2.09
article

Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus

Yanhong Shi, Hongrui Chen, Yuhang Zhang, Yunming Wang, Yilin Zhong
article en
6 citations

Abstract

Abstract Formation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter‐UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus‐based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter‐UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

IET Control Theory and ApplicationsVol. 18(18)
Nanjing Foreign Language School (CN), Dalian Jiaotong University (CN)
Openalex Percentile: Top 12%
Distributed Control Multi-Agent Systems
2.09
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Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus — Yanhong Shi, Hongrui Chen, et al. · IET Control Theory and Applications (2024) | TGRS Research Map | TGRS