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The consensus control is a key issue in the research of unmanned aerial vehicle (UAV) swarm. Molecular dynamics is a method of stimulating complex group emergence behavior through simple interactions between individuals. This article draws inspiration from the principles of molecular dynamics to building a collective dynamics model for UAV swarm. Firstly, using Vicsek model to construct the self-organizing motion computational model. Then, using the Lennard-Jones two-body interaction potential in molecular dynamics as the interaction law of UAV swarm. By analyzing the Newtonian mechanical forces between each UAV in the swarm, consensus control of the UAV swarm can be achieved. Finally, simulation verification was conducted to analyze the effects of different power potential functions, different scales of swarm, and different white noise coefficient on the consensus control of UAV swarm. It is concluded that the 8–4 potential has better results for consensus control of UAV swarm and has good control effects on small, medium, and large scales UAV swarm. However, consensus cannot be fully guaranteed under strong noise conditions.

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Distributed Consensus Control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential

  • 会议论文

  • 发表日期：

    2025年

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  • Peng Xu,
  • Jintao Liu,
  • Xuefei Sun,
  • Haotian Chen,
  • Yinsheng Chen

摘要

The consensus control is a key issue in the research of unmanned aerial vehicle (UAV) swarm. Molecular dynamics is a method of stimulating complex group emergence behavior through simple interactions between individuals. This article draws inspiration from the principles of molecular dynamics to building a collective dynamics model for UAV swarm. Firstly, using Vicsek model to construct the self-organizing motion computational model. Then, using the Lennard-Jones two-body interaction potential in molecular dynamics as the interaction law of UAV swarm. By analyzing the Newtonian mechanical forces between each UAV in the swarm, consensus control of the UAV swarm can be achieved. Finally, simulation verification was conducted to analyze the effects of different power potential functions, different scales of swarm, and different white noise coefficient on the consensus control of UAV swarm. It is concluded that the 8–4 potential has better results for consensus control of UAV swarm and has good control effects on small, medium, and large scales UAV swarm. However, consensus cannot be fully guaranteed under strong noise conditions.