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首页> 外文期刊>The Aeronautical Journal >Estimation of aerodynamic parameters near stall using maximum likelihood and extreme learning machine-based methods
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Estimation of aerodynamic parameters near stall using maximum likelihood and extreme learning machine-based methods

机译：利用最大似然和基于极端学习机的方法估算停滞附近的空气动力学参数

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摘要

The stability and control derivatives are essential parameters in the flight operation of aircraft, and their determination is a routine task using classical parameter estimation methods based on maximum likelihood and least-squares principles. At high angle-of-attack, the unsteady aerodynamics may pose difficulty in aerodynamic structure determination, hence data-driven methods based on artificial neural networks could be an alternative choice for building models to characterise the behaviour of the system based on the measured motion and control variables. This research paper investigates the feasibility of using a recurrent neural model based on an extreme learning machine network in the modelling of the aircraft dynamics in a restricted sense for identification of the aerodynamic parameters. The recurrent extreme learning machine network is combined with the Gauss-Newton method to optimise the unknowns of the postulated aerodynamic model. The efficacy of the proposed estimation algorithm is studied using real flight data from a quasi-steady stall manoeuvre. Furthermore, the estimates are validated against the parameters estimated using the maximum likelihood method. The standard deviations of the estimates demonstrate the effectiveness of the proposed algorithm. Finally, the quantities regenerated using the estimates present good agreement with their corresponding measured values, confirming that a qualitative estimation can be obtained using the proposed estimation algorithm.
机译：稳定性和控制衍生物是飞机飞行运行中的基本参数，它们的确定是使用基于最大可能性和最小二乘原理的经典参数估计方法的例程任务。在高角度攻角中，不稳定的空气动力学可能在空气动力学结构确定中造成困难，因此基于人工神经网络的数据驱动方法可以是构建模型的替代选择，以基于测量的运动来表征系统的行为。控制变量。本研究论文研究了使用基于极端学习机网络在飞机动力学建模中使用反复性神经模型的可行性，以识别空气动力学参数。经常性极限学习机网络与高斯 - 牛顿方法相结合，以优化假设空气动力学模型的未知数。使用来自准稳定的摊位机动的真正飞行数据研究了所提出的估计算法的功效。此外，估计是根据使用最大似然法估计的参数验证的。估计的标准偏差证明了所提出的算法的有效性。最后，使用估计重新生成的数量与其相应的测量值具有良好的一致性，确认可以使用所提出的估计算法获得定性估计。

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