THE effects of flexibility on the flight dynamics of large transport aircraft have been shown to be quite significant, especially as the frequencies of the elastic modes become lower and approach those of the rigid body modes [1]. The handling characteristics of such vehicles are altered significantly from those of a rigid vehicle, and the design of the flight-control system may become drastically more complex. Therefore, mathematical modeling of an aeroelastic
vehicle for dynamic analysis and control system design is a major issue in flight vehicle dynamics. Consequently, the need to model accurately the dynamics of such vehicles is becoming inevitable. Parameter estimation from flight data, as applied to aircraft in the linear flight regime, is currently being used on a routine basis with the assumption that the rigid body model is valid [2]. Elastic degrees of freedom are, therefore, absent from the aircraft derivative model used in the estimation algorithm. However, the newly introduced highly maneuverable aircraft with a high degree of flexibility poses a new challenge to search for appropriate aeroelastic models for inclusion in parameter estimation algorithms [3]. A simplified integrated
modeling approach to account for the aeroelastic effects in aircraft dynamics was suggested in [4], and its use for a highly elastic aircraft was demonstrated. The aeroelastic model of [4] was simplified in [5] and the resulting model, with a reduced number of unknown terms, was used in the identification of the aeroelastic aircraft. In another
attempt, parameter estimation from simulated data of a flexible aircraft was carried out by defining the aerodynamic derivatives in a linear form with the quasi-steady aeroelastic effects represented through the flex factor [6].
[1]
David K. Schmidt,et al.
Flight dynamics of aeroelastic vehicles
,
1988
.
[2]
Alfonso C. Paris,et al.
Nonlinear Model Development from Flight-Test Data for F/A-18E Super Hornet
,
2003
.
[3]
A. K. Ghosh,et al.
Parameter estimates of an aeroelastic aircraft as affected by model simplifications
,
1993
.
[4]
Robert F. Stengel,et al.
Identification of aerodynamic coefficients using computational neural networks
,
1993
.
[5]
Robert Hess,et al.
On the use of back propagation with feed-forward neural networks forthe aerodynamic estimation problem
,
1993
.
[6]
J. E. Murray,et al.
Flight testing a highly flexible aircraft - Case study on the MIT Light Eagle
,
1990
.
[7]
Ajoy Kanti Ghosh,et al.
Parameter estimation of an aeroelastic aircraft using neural networks
,
2000
.
[8]
Colin R. Theodore,et al.
System Identification of Large Flexible Transport Aircraft
,
2008
.