Aircraft lateral parameter estimation from flight data with unsteady aerodynamic modelling

Aircraft Lateral Parameter Estimation from Flight Data with Unsteady Aerodynamic Modeling

Journal of Aircraft ◽

10.2514/3.57377 ◽

1982 ◽

Vol 19 (3) ◽

pp. 206-210 ◽

Cited By ~ 1

Author(s):

William R. Wells ◽

Siva S. Banda ◽

David L. Quam

Keyword(s):

Parameter Estimation ◽

Flight Data ◽

Aerodynamic Modeling ◽

Unsteady Aerodynamic

Download Full-text

GPR-based novel approach for non-linear aerodynamic modelling from flight data

The Aeronautical Journal ◽

10.1017/aer.2018.114 ◽

2018 ◽

Vol 123 (1259) ◽

pp. 79-92

Author(s):

A. Kumar ◽

A. K. Ghosh

Keyword(s):

Aerodynamic Force ◽

Equations Of Motion ◽

Gaussian Process Regression ◽

Likelihood Estimation ◽

Flight Data ◽

Novel Approach ◽

Non Linear ◽

Research Aircraft ◽

Novel Method ◽

Aerodynamic Modelling

ABSTRACTIn this paper, a Gaussian process regression (GPR)-based novel method is proposed for non-linear aerodynamic modelling of the aircraft using flight data. This data-driven regression approach uses the kernel-based probabilistic model to predict the non-linearity. The efficacy of this method is examined and validated by estimating force and moment coefficients using research aircraft flight data. Estimated coefficients of aerodynamic force and moment using GPR method are compared with the estimated coefficients using maximum-likelihood estimation (MLE) method. Estimated coefficients from the GPR method are statistically analysed and found to be at par with estimated coefficients from MLE, which is popularly used as a conventional method. GPR approach does not require to solve the complex equations of motion. GPR further can be directed for the generalised applications in the area of aeroelasticity, load estimation, and optimisation.

Download Full-text

Drag Coefficient Identification from Flight Data via Optimal Observer

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.787 ◽

2014 ◽

Vol 687-691 ◽

pp. 787-790

Author(s):

Rong Jun Yang ◽

Yao Ye

Keyword(s):

Parameter Estimation ◽

Kalman Filter ◽

Drag Coefficient ◽

Dynamic Equation ◽

Unscented Kalman Filter ◽

Flight Test ◽

Measurement Noise ◽

Estimation Technique ◽

Flight Data ◽

Coefficient Identification

. 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.

Download Full-text