基于非梯度估计的三角翼无人机气动特性研究

N. Kumar, S. Saderla, Y. Kim
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引用次数: 0

摘要

飞行试验气动特性是评价新型飞行器气动性能、稳定性和可控性的重要子程序。从飞行试验数据中估计气动参数已经得到了广泛的探索,过去主要采用方程误差法、输出误差法和滤波误差法等估计方法。然而,在当前时代,非梯度估计技术由于其固有的数据驱动优化能力而受到研究人员的关注,以寻找全局最优解。本文提出了一种基于粒子群优化的极大似然方法,基于飞行数据对无人机的气动特性进行非梯度估计。最后,利用一架单翼无人机的飞行数据集验证了该方法在估算气动导数方面的能力。通过风洞试验和输出误差法验证了该方法的估计结果。已经观察到,在大多数考虑的机动中,使用估计参数的模拟飞行器响应与测量数据很好地一致。线性和非线性气动参数估计的置信度是用最小的Cramer-Rao界下限建立的。该方法通过估算翼型静态失速特性参数、滞后时间常数和断点等失速特性参数,证明了准稳态失速气动模型具有良好的可预测性。提出的估计方法的总体性能与输出误差方法相当,并通过匹配证明练习进行了验证。
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Aerodynamic characterisation of delta wing unmanned aerial vehicle using non-gradient-based estimator
Aerodynamic characterisation from flight testing is an integral subroutine for evaluating a new flight vehicle’s aerodynamic performance, stability and controllability. The estimation of aerodynamic parameters from flight test data has extensively been explored, in the past, using estimation methods such as the equation error method, output error method and filter error method. However, in the current era, non-gradient-based estimation techniques are gaining attention from researchers due to their inherent data-driven optimisation capability to find the global best solution. In this paper, a novel non-gradient-based estimation method is proposed for the aerodynamic characterisation of unmanned aerial vehicles from flight data, which relies on the maximum likelihood method augmented with particle swarm optimisation. Flight data sets of a wing-alone unmanned aerial vehicle are used to demonstrate the capabilities of the proposed method in estimating aerodynamic derivatives. Estimates from the proposed method are corroborated with the wind tunnel test and output error method results. It has been observed that simulated flight vehicle responses using estimated parameters are in good agreement with measured data in most of the manoeuvers considered. Confidence in the estimates of linear and nonlinear aerodynamic parameters is well established with the lower limit of Cramer-Rao bounds, which are minimal. The proposed method also demonstrates good predictability of the quasi-steady stall aerodynamic model by estimating stall characteristic parameters such as aerofoil static stall characteristics parameter, hysteresis time constant and breakpoint. The overall performance of the proposed estimation method is on par with the output error method and is validated with the proof-of-match exercise.
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