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Receding horizon control for multiple UAV formation flight based on modified brain storm optimization

机译:基于改进脑风暴优化的多架无人机编队飞行后视控制

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摘要

Formation flight for unmanned aerial vehicles (UAVs) is a rather complicated global optimum problem. In the global optimum problem, the complex relationship between the controller parameters and the performance index, and the different kinds of constrains under complex combat field environment are taken into account.Brain storm optimization (BSO) is a brand-new swarm intelligence optimization algorithm inspired by a human being's behavior of brainstorming. In this paper, in allusion to the drawbacks that the basic BSO algorithm traps into local optimum easily and has a slow convergent speed, some novel designs are proposed to enhance the performance of the optimization algorithm. The modified BSO is applied to solve the optimization problem based on the nonlinear Receding horizon control (RHC) mode of UAVs to seek the RHC control parameters for UAV formation flight. Series of comparative experimental results are presented to show the feasibility, validity, and superiority of our proposed method.
机译:无人机的编队飞行是一个相当复杂的全球最优问题。在全局最优问题中,考虑了控制器参数与性能指标之间的复杂关系以及复杂战场环境下的各种约束。脑风暴优化(BSO)是一种全新的群体智能优化算法。通过人类的头脑风暴行为。针对传统的BSO算法容易陷入局部最优,收敛速度慢的缺点,提出了一些新颖的设计来提高优化算法的性能。改进后的BSO算法被用于解决基于无人机的非线性后视水平控制(RHC)模式的优化问题,以寻求用于无人机编队飞行的RHC控制参数。提出了一系列的对比实验结果,以证明我们提出的方法的可行性,有效性和优越性。

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