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Piecewise Polynomial Model Identification using Constrained Least Squares for UAS Stall

机译:分段多项式模型识别使用UAS摊位的约束最小二乘法

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In this paper, we propose a new piecewise polynomial model (PwPM) for the modelling of an Unmanned Aircraft System (UAS) aerodynamic coefficients over a wide flight envelope, where they are characterized by nonlinear and hysteresis phenomena. An associated identification method using Constrained Least Squares (CLS) is then presented and successfully applied to experimental data obtained from wind tunnel measurements. The resulting fitting is finally compared with the similar piecewise models PWPFIT and AERODAS, showing a greater precision in its modelling, while maintaining their relative simplicity and computation performance.
机译:在本文中,我们提出了一种新的分段多项式模型(PWPM),用于在宽飞行包络上的无人机系统(UAS)空气动力学系数的建模,其中它们的特征在于非线性和滞后现象。 然后呈现使用约束最小二乘(CLS)的相关识别方法并成功地应用于从风隧道测量获得的实验数据。 最终将所得拟合与PWPFIT和Aerodas类似的分段模型进行比较,在其建模中显示出更高的精度,同时保持其相对简单性和计算性能。

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