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Accelerated Particle Swarm Optimization for Endurance of MALE UAV

机译:加速粒子群算法优化MALE无人机的续航能力

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One of the most daunting task in the design of a medium altitude long endurance (MALE) unmanned aerial vehicles (UAV s) is the selection of the airframe parameters to give maximum possible endurance while satisfying the design constraints. In this paper, we have researched the endurance problem of MALE UAVs by casting it as an optimization problem. To tackle this, we have defined an endurance equation for the UAV in terms of the design parameters and then used accelerated particle swarm optimization (APSO) to get the optimal values for the airframe parameters. The considered design parameters are the dimensions of the UAV geometrical components which have been represented in terms of wing span and mean aerodynamic chord. The design constraints are airframe geometry, its dimension and UAV mass. The design geometry for the optimal parameters has been modeled and analyzed using XFLR5.
机译:在中等高度长时间耐力(MALE)无人机(UAV s)设计中,最艰巨的任务之一是选择机身参数,以在满足设计约束的同时提供最大的耐力。在本文中,我们通过将其作为优化问题来研究了MALE无人机的耐久性问题。为了解决这个问题,我们根据设计参数定义了无人机的续航力方程,然后使用加速粒子群算法(APSO)获得了机身参数的最佳值。所考虑的设计参数是无人机几何部件的尺寸,这些尺寸已用机翼跨度和平均空气动力弦表示。设计约束是机身几何形状,其尺寸和无人机质量。使用XFLR5对最佳参数的设计几何图形进行了建模和分析。

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