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Identification of Flight Vehicle Models Using Fuzzified Eigensystem Realization Algorithm

机译:基于模糊特征系统实现算法的飞行器模型辨识

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摘要

This paper presents a new approach with a fuzzified eigensystem realization algorithm for identification of flight vehicle models in low-speed wind tunnel (LSWT) and high-speed wind tunnel (HSWT). A variety of variables in model types and testing environment (such as angle-of-attack, sideslip angle, tunnel wind speed) and profile, elevator, and power system (motor and propeller) of mini unmanned aerial vehicle (mini-UAV) model are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify the effectiveness of the proposed methodology, simulations are conducted using real-world experimental results that demonstrate that the working performance of the proposed method correlates well as expected.
机译:本文提出了一种具有模糊特征系统实现算法的新方法,用于识别低速风洞(LSWT)和高速风洞(HSWT)中的飞行器模型。微型无人机模型的模型类型和测试环境中的各种变量(例如攻角,侧滑角,隧道风速)以及轮廓,电梯和动力系统(电动机和螺旋桨)在LSWT的开机微型无人飞行器测试系统和HSWT的航空航天研究与开发顾问组(AGARD)标准校准模型中进行了考虑。基于模糊逻辑推理结构的方法简单有效。将获得的结果与通过常规风洞测试方法获得的结果进行比较。为了验证所提出方法的有效性,使用真实世界的实验结果进行了仿真,结果表明所提出方法的工作性能与预期的相关性良好。

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