A Novel Flight Dynamics Modeling Using Robust Support Vector
Regression against Adversarial Attacks 01-16-03-0019
This also appears in
SAE International Journal of Aerospace-V132-1EJ
An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to
design its efficient controller in early development phases and to increase
safety while reducing costs. Flight tests are normally conducted for a
pre-established number of flight conditions, and then mathematical methods are
used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl,
216 local FDMs corresponding to different flight conditions were utilized to
create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial
flight envelope data containing 216 local FDMs was further augmented using
interpolation and extrapolation methodologies, thus increasing the number of
trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the
Support Vector Machine (SVM) methodology was used as a benchmarking regression
algorithm due to its excellent performance when training samples could not be
separated linearly. The trained Support Vector Regression (SVR) predicted the
FDM for the entire flight envelope. Although the SVR-FDM showed excellent
performance, it remained vulnerable to adversarial attacks. Hence, we modified
it using an adversarial retraining defense algorithm by transforming it into a
Robust SVR-FDM. For validation studies, the quality of predicted UAS-S4 FDM was
evaluated based on the Root Locus diagram. The closeness of predicted
eigenvalues to the original eigenvalues confirmed the high accuracy of the
UAS-S4 SVR-FDM. The SVR prediction accuracy was evaluated at 216 flight
conditions, for different numbers of neighbors, and a variety of kernel
functions were also considered. In addition, the regression performance was
analyzed based on the step response of state variables in the closed-loop
control architecture. The SVR-FDM provided the shortest rise time and settling
time, but it failed when adversarial attacks were imposed on the SVR. The
Robust-SVR-FDM step response properties showed that it could provide more
accurate results than the LLS-FDM approach while protecting the controller from
adversarial attacks.
Citation: Hashemi, S. and Botez, R., "A Novel Flight Dynamics Modeling Using Robust Support Vector Regression against Adversarial Attacks," SAE Int. J. Aerosp. 16(3):305-323, 2023, https://doi.org/10.4271/01-16-03-0019. Download Citation
Author(s):
Seyed Mohammad Hashemi, Ruxandra Mihaela Botez
Affiliated:
University of Quebec, École de technologie supérieure, Canada
Pages: 20
ISSN:
1946-3855
e-ISSN:
1946-3901
Related Topics:
Flight dynamics
Unmanned aerial vehicles
Mathematical models
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