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Estimation of lateral-directional parameters using neural networks based modified delta method

机译：基于神经网络的改进增量法估计横向参数

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摘要

The aim of the study described herein was to develop and verify an efficient neural network based method for extracting aircraft stability and control derivatives from real flight data using feed-forward neural networks. The proposed method (Modified Delta method) draws its inspiration from feed forward neural network based the Delta method for estimating stability and control derivatives. The neural network is trained using differential variation of aircraft motion/control variables and coefficients as the network inputs and outputs respectively. For the purpose of parameter estimation, the trained neural network is presented with a suitably modified input file and the corresponding predicted output file of aerodynamic coefficients is obtained. An appropriate interpretation and manipulation of such input-output files yields the estimates of the parameter. The method is validated first on the simulated flight data using various combinations and types of real-flight control inputs and then on real flight data. A new technique is also proposed for validating the estimated parameters using feed-forward neural networks.
机译：本文所述研究的目的是开发和验证基于有效神经网络的方法，该方法使用前馈神经网络从实际飞行数据中提取飞机稳定性和控制导数。所提出的方法（改进的Delta方法）从基于Delta方法的前馈神经网络中获取稳定性和控制导数的启发。使用飞机运动/控制变量和系数的微分变化分别作为网络的输入和输出来训练神经网络。出于参数估计的目的，为训练后的神经网络提供了经过适当修改的输入文件，并获得了相应的空气动力学系数预测输出文件。对此类输入输出文件的适当解释和操作会得出参数的估计值。首先使用各种组合和类型的实际飞行控制输入对模拟飞行数据进行验证，然后对实际飞行数据进行验证。还提出了一种使用前馈神经网络来验证估计参数的新技术。

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