Aircraft Lateral Parameter Estimation from Flight Data with Unsteady Aerodynamic Modeling

1982 ◽  
Vol 19 (3) ◽  
pp. 206-210 ◽  
Author(s):  
William R. Wells ◽  
Siva S. Banda ◽  
David L. Quam
2016 ◽  
Vol 53 (5) ◽  
pp. 1261-1297 ◽  
Author(s):  
Jay M. Brandon ◽  
Eugene A. Morelli

2022 ◽  
Author(s):  
Huseyin E. Tekaslan ◽  
Yusuf Demiroglu ◽  
Melike Nikbay

2022 ◽  
Author(s):  
James L. Gresham ◽  
Benjamin M. Simmons ◽  
Jeremy W. Hopwood ◽  
Craig A. Woolsey

2018 ◽  
Vol 10 (6) ◽  
pp. 063304 ◽  
Author(s):  
Wenguang Zhang ◽  
Yifeng Wang ◽  
Ruijie Liu ◽  
Haipeng Liu ◽  
Xu Zhang

2014 ◽  
Vol 602-605 ◽  
pp. 3140-3143
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao

To understand the characteristics of aircraft stall for better aerodynamic model, the physical essence of the stall phenomena of aircraft is first introduced, and then a Wavelet Neural Network (WNN) is proposed to set up the stall aerodynamic model. Numerical examples indicates that through the deep cognition of the stall phenomena of aircraft the proposed stall aerodynamic method has a better accuracy than the traditional neural network and is also effective and feasible.


2014 ◽  
Vol 687-691 ◽  
pp. 787-790
Author(s):  
Rong Jun Yang ◽  
Yao Ye

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.


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