The aerodynamic force estimation of a swept-wing UAV using ANFIS based on metaheuristic algorithms

M. Uzun, H. H. Bilgic, E. H. Çopur, S. Çoban
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Abstract

In this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. Metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.
基于元启发式算法的后掠翼无人机气动力估计
本文提出了一种新的变形无人机建模与控制方法。在研究范围内,利用计算流体动力学方法,利用Ansys Fluent软件获取可变条件下无人机的广泛气动参数,创建了一个数据集。为此,我们创建了一个大型数据集,考虑了5种不同的攻角、14种不同的掠角和5种不同的速度。在创建数据集时,通过考虑已在文献中得到实验验证的研究来验证分析。然后,利用获得的数据集建立基于人工智能的模型。采用人工蜂群算法、蚁群算法和遗传算法等元启发式算法来提高自适应神经模糊推理系统(ANFIS)方法的建模成功率。提出了一种新的建模方法,构成了一个新的实时飞行决策支持系统。结果表明,基于元启发式算法的ANFIS模型均优于传统的多元线性回归模型。利用所设计的决策支持系统,对不同飞行条件下满足无人机最小升力的掠角进行了估计。因此,阻力是最小的,同时获得所需的升力。将无人机的性能与非变形构型进行了比较,并以图表的形式给出了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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