Estimation of Stability and Control Derivatives of Light Canard Research Aircraft from Flight Data

High-fidelity rotorcraft flight simulation relies on the availability of a quality flight model that further demands a good level of understanding of the complexities arising from aerodynamic couplings and interference effects. One such example is the difficulty in the prediction of the characteristics of the rotorcraft lateral-directional oscillation (LDO) mode in simulation. Achieving an acceptable level of the damping of this mode is a design challenge requiring simulation models with sufficient fidelity that reveal sources of destabilizing effects. This paper is focused on using System Identification to highlight such fidelity issues using Liverpool's FLIGHTLAB Bell 412 simulation model and in-flight LDO measurements from the bare airframe National Research Council's (Canada) Advanced Systems Research Aircraft. The simulation model was renovated to improve the fidelity of the model. The results show a close match between the identified models and flight test for the LDO mode frequency and damping. Comparison of identified stability and control derivatives with those predicted by the simulation model highlight areas of good and poor fidelity.

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Estimating open-loop stability and control derivatives for the Tu-144LL supersonic transport from flight data

10.2514/6.2000-3901 ◽

2000 ◽

Cited By ~ 1

Author(s):

Michael Lensi

Keyword(s):

Open Loop ◽

Flight Data ◽

Supersonic Transport ◽

Stability And Control ◽

Control Derivatives ◽

And Control ◽

Loop Stability

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

The Aeronautical Journal ◽

10.1017/s0001924000004838 ◽

2007 ◽

Vol 111 (1124) ◽

pp. 659-667 ◽

Cited By ~ 19

Author(s):

S. Singh ◽

A. K. Ghosh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Flight Control ◽

Delta Method ◽

Feed Forward ◽

Flight Data ◽

Stability And Control ◽

Control Derivatives ◽

Feed Forward Neural Networks ◽

And Control

Abstract 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.

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Aerodynamic Parameters Estimation Using Radial Basis Function Neural Partial Differentiation Method

Defence Science Journal ◽

10.14429/dsj.68.11843 ◽

2018 ◽

Vol 68 (3) ◽

pp. 241 ◽

Cited By ~ 2

Author(s):

Jitu Sanwale ◽

Dhan Jeet Singh

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Flight Data ◽

Stability And Control ◽

Partial Differentiation ◽

Radial Basis ◽

Control Derivatives ◽

The Stability ◽

And Control

Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation of stability and control derivatives from recorded flight data. This problem is extensively studied in the past using classical approaches such as output error, filter error and equation error methods. An alternative approach to these model based methods is the machine learning such as artificial neural network. In this paper, radial basis function neural network (RBF NN) is used to model the lateral-directional force and moment coefficients. The RBF NN is trained using k-means clustering algorithm for finding the centers of radial basis function and extended Kalman filter for obtaining the weights in the output layer. Then, a new method is proposed to obtain the stability and control derivatives. The first order partial differentiation is performed analytically on the radial basis function neural network approximated output. The stability and control derivatives are computed at each training data point, thus reducing the post training time and computational efforts compared to hitherto delta method and its variants. The efficacy of the identified model and proposed neural derivative method is demonstrated using real time flight data of ATTAS aircraft. The results from the proposed approach compare well with those from the other.

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