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A modified bacterial-foraging tuning algorithm for multimodal optimization of the flight control system

机译：用于飞行控制系统多模式优化的改进的细菌觅食调整算法

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The flight control parameter tuning of an aircraft is a tedious task even for experienced engineers. In this paper, a Modified Bacterial Foraging Optimization (MBFO) algorithm is developed to deal with control parameter tuning problems and explore the inner relationship between control parameters and the aircraft Flight Control System (FCS). In the proposed method, a serial of bacteria is first assigned to the searching space according to the quasi-Monte Carlo sampling method. Then, the whole colony is divided into several search groups according to the dynamic k-means clustering method. The bacteria are updated according to the nutrient variation in the environment and are always moving towards the areas with more food sources. Moreover, a health assessment strategy is adopted to eliminate bacteria with weak searching abilities and replace them with healthy bacteria to maintain the robust searching ability of the whole colony. Finally, a variety of feasible solutions is illustrated and comparative results are analyzed. A longitudinal FCS of the F/A-18 model designed for aircraft automatic landing is used as a test bed to carry out simulation studies to demonstrate the effectiveness of the proposed method. (C) 2019 Elsevier Masson SAS. All rights reserved.
机译：即使对于有经验的工程师来说，飞机的飞行控制参数调整也是一项繁琐的任务。本文提出了一种改进的细菌觅食优化算法（MBFO）来处理控制参数调整问题，并探讨控制参数与飞机飞行控制系统（FCS）之间的内在联系。在所提出的方法中，首先根据准蒙特卡洛采样方法将一系列细菌分配给搜索空间。然后，根据动态k均值聚类方法将整个菌落分为几个搜索组。细菌会根据环境中的养分变化进行更新，并且始终朝着食物来源更多的地区发展。此外，采用健康评估策略消除搜索能力较弱的细菌，并用健康细菌替代它们，以维持整个菌落的强大搜索能力。最后，说明了各种可行的解决方案，并对比较结果进行了分析。为飞机自动降落设计的F / A-18模型的纵向FCS用作测试台，以进行仿真研究，以证明所提出方法的有效性。 （C）2019 Elsevier Masson SAS。版权所有。

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