The research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

中文翻译：

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使用极限学习机和高斯-牛顿算法的飞机参数估计

该研究论文解决了使用基于 Gauss-Newton 的优化方法估计空气动力学参数的问题。优化方法的过程基于最小化系统测量响应和模拟响应之间的残余误差的原则。通常，模拟响应是通过对系统的动力学方程进行积分而获得的，该方程被发现易受初始值的影响，并采用积分方法。随着前馈神经网络的出现，数据驱动的回归方法已被广泛用于系统的识别。其中，前馈神经网络的一种变体，极限学习机，已经证明了在计算成本、泛化等方面的性能，已被用于预测本研究中的响应。考虑了纵向和横向运动的真实飞行数据来估计它们各自的气动参数。此外，估计已经用经典估计方法的值进行了验证，例如方程误差和滤波器误差方法。估计的样本标准差证明了所提出方法的有效性。最后，已经使用另一组飞行数据进行了匹配证明练习，以验证估计的参数。估计的样本标准差证明了所提出方法的有效性。最后，已经使用另一组飞行数据进行了匹配证明练习，以验证估计的参数。估计的样本标准差证明了所提出方法的有效性。最后，已经使用另一组飞行数据进行了匹配证明练习，以验证估计的参数。