Close formation flight of swarm unmanned aerial vehicles (UAVs) has drawn much attention from scholars due to its significant importance in many aspects. In this paper, we focus on an advanced controller design for swarm UAV close formation based on a novel bio-inspired algorithm, i.e., metric-distance brain storm optimization (MDBSO). The proposed method utilizes the brain storm optimization (BSO) which has been extensively adopted in complicated systems with great performances and modifies its basic operators to formulate the formation flight controller design. The original clustering operator in BSO is replaced by a fresh clustering method based on metric distances, while the individual updating operator utilizes Lévy distribution to extend search steps to fit into the metric searching regions. Then the proposed algorithm is applied to optimize the benchmark controller in swarm UAV close formation to enhance the tracking performances under complicated circumstances. Simulation results demonstrate that our approach is more superior in stable configuration of swarm UAV close formations by comparing with several generic methods.

中文翻译：

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通过公制距离脑风暴优化的群无人飞行器近距离编队飞行

群体无人飞行器（UAV）的近距离编队飞行由于其在许多方面的重要性而备受学者的关注。在本文中，我们专注于基于新型生物启发算法（即度量距离脑风暴优化（MDBSO））的群体无人机紧密编队的高级控制器设计。所提出的方法利用了脑风暴优化（BSO）技术，该技术已在复杂系统中被广泛采用，并具有出色的性能，并修改了其基本算子以制定编队飞行控制器设计。BSO中的原始聚类运算符被基于度量距离的全新聚类方法所取代，而各个更新运算符则利用Lévy分布扩展搜索步骤以适合度量搜索区域。然后将所提出的算法应用于群无人机无人机编队中的基准控制器的优化，以增强复杂情况下的跟踪性能。仿真结果表明，与几种通用方法相比，我们的方法在群体无人机紧密编队的稳定配置方面更具优势。