Title :
State space identification of flight dynamics models
Author_Institution :
Dept. of Aircraft Control Syst., Nat. Aviation Univ., Kiev, Ukraine
Abstract :
This paper is devoted to the state space identification of the flight dynamics models in the presence of sensor noise and biases. The goal of the identification procedure is not only the estimation aircraft stability and control derivatives, but also the biases of sensors. It is achieved by using the procedure of the likelihood function minimization, based on the Kalman filter and the stochastic approximation procedure. The application technique of the least-squares method to a state space model in order to determine initial values of unknown parameters which are necessary to identify the state space model by maximum likelihood method is created. This procedure was used for state space identification of the model of lateral-directional dynamics of small 6-seat aircraft and results of this identification are presented.
Keywords :
aircraft control; approximation theory; least squares approximations; maximum likelihood estimation; mechanical stability; minimisation; sensors; state-space methods; vehicle dynamics; 6-seat aircraft; Kalman filter; aircraft control derivative; aircraft stability dervative; flight dynamics model; lateral-directional dynamics; least squares method; likelihood function minimization; maximum likelihood method; sensor bias; sensor noise; state space identification; stochastic approximation procedure; Approximation methods; Atmospheric modeling; Data processing; Noise; Technological innovation; State space identification; aircraft model; least squared method; maximum likelihood function; sensor bias;
Conference_Titel :
Methods and Systems of Navigation and Motion Control (MSNMC), 2012 2nd International Conference
Conference_Location :
Kiev
Print_ISBN :
978-1-4673-2551-6
DOI :
10.1109/MSNMC.2012.6475108