Purpose

The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.

Design/methodology/approach

Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.

Findings

Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.

Research limitations/implications

The proposed method requires iterative calculation and can only identify parameters offline.

Practical implications

The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.

Originality/value

In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

中文翻译：

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使用 Levenberg-Marquardt 算法和网络进行空气动力学系数建模

目的

本文的目的是提出一种新的基于神经网络和输出误差法的气动参数估计方法，同时基于粒子群算法对输出误差法进行改进。

设计/方法/方法

该算法首先基于前馈神经网络逼近飞行器的动态特性。神经网络由极限学习机训练，训练后的网络可以根据第 k 时刻测量的飞行数据预测飞机在第 ( k  + 1) 时刻的响应。其次，粒子群优化用于增强Levenberg-Marquardt（LM）算法的收敛性，改进的LM方法用于替代输出误差法中的高斯牛顿算法。最后，将训练好的神经网络与改进的输出误差法相结合，估计气动导数。

发现

该算法既不依赖于要估计的参数的初始猜测，也不要求对飞机运动方程进行数值积分，该算法可用于不稳定的飞机，并成功地应用于从模拟和真实飞行数据中提取气动导数。

研究限制/影响

该方法需要迭代计算，只能离线识别参数。

实际影响

该方法成功地应用于飞机气动参数的估计，也可以作为其他优化问题的新算法。

原创性/价值

本研究对输出误差法进行了改进，以减少对参数初始值的依赖，扩大其应用范围。它与神经网络一起应用于飞机气动参数识别。