Aircraft dynamic model identification on the basis of flight data recorder registers
Abstract: We investigate the problem of an aircraft dynamic model parametric identification using dimensional derivatives as an example. Identification is done in offline mode, in the time domain. Flight parameters used for identification are obtained from Flight Data Recorder, that register them during each scheduled flight. We investigate the possibility of application of Maximum Likelihood Estimation that belongs to the Output Error Methods class. The likelihood function is defined for n-dimensional multivariate normal distribution. Unknown covariance matrix is estimated with the use of measured data and output equation. Output equation is calculated with Runge–Kutta fourth order method. In order to find the cost function minimum we consider using Levenberg-Marquardt Algorithm, where derivatives are calculated with central difference formulas and small perturbations theory. Mathematical model of an aircraft is obtained through flight dynamics classical approach. Rigid body model of an aircraft is assumed. Coordinate Systems Transformations are done using Euler’s Rotation Theorem with angle order typical for flight dynamics. Equations of motion are obtained from Newtons Second Law of Motion in body fixed coordinate system Oxyz, that is located at aircraft’s center of gravity. Turbulence is modeled as a bias, and also is an object of identification. We implement this method in Matlab R2009b environment.
Keywords: Dimensional derivatives; Estimation; Flight Data Recorder; Flight Dynamics; Maximum Likelihood Estimation; Levenberg-Marquardt; System Identification
Area: Mechanics, Automation and Robotics
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