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GA-Based Flight Motion Model Parameter Identification of a Subminiature Fixed-Wing Unmanned Aerial Vehicle

机译:基于遗传算法的超小型固定翼无人机飞行模型参数辨识

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Subminiature fixed-wing unmanned aerial vehicles (SUAVs) present an enormous potential for low-altitude exploration applications. In order to develop a robotic SUAV and realize high level of autonomy, the flight motion model of the platform with wingspan of 1.8m is studied thoroughly. Based on the rectilinear trim flight and small perturbation theory, the linear parametric model is investigated. The flight experiments are designed, and the flight data measurement system is developed to collect the input and output data. Through outliers processing, data smoothing, data fusion, etc, all the required data for parameter identification are figured out. As the estimation method, improvements of GA are detailed, and the comparison of model verification indicates that improved GA has prominent effects in flight motion model parameter identification of SUAV.
机译:超小型固定翼无人机(SUAV)为低空探索应用提供了巨大潜力。为了开发机器人SUAV并实现高度自治,对翼展为1.8m的平台的飞行运动模型进行了深入研究。基于直线微调飞行和小扰动理论,研究了线性参数模型。设计了飞行实验,并开发了飞行数据测量系统以收集输入和输出数据。通过离群值处理,数据平滑,数据融合等,可以找出用于参数识别的所有必需数据。作为估计方法,详细介绍了遗传算法的改进,并通过模型验证的比较表明,改进的遗传算法在SUAV飞行运动模型参数辨识中具有显着效果。

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