Algorihtm of state space identification of aircraft model

A. M. Klipa

Abstract


This paper is devoted to the algorithm of 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 of 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 leastsquares 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 algorithm was used for state space identification of the model of lateraldirectiona dynamics of small 6-seat aircraft


Keywords


state space identification; aircraft model; sensor bias; least-squares method; maximum likelihood function

References


Ljung L. System Identification. Theory for the User. Prentice / L. Ljung. – Hall, Inc., 1987 p.

Jategaonkar R. Algorithms for aircraft parameter estimation accounting for process and measurement noise / R. Jategaonkar, E. Plaetschke // Journal of Aircraft. – 1989. – Р. 360–372.

Maine R. Formulation and Implementation of a Practical Algorithm for Parameter Estimation with Process and Measurement Noise / R. Maine, K. Illif // Society of Industrial and Applied Mathematics, Journal of Applied Mathematics. – 1981. – Vol. 41, No. 3. – P. 558–579.

Tunik A. The Identification of the Flight Dynamics Models with Biased Sensors / A. Tunik, A. Klipa // Stability and Control Theory and Applications. – 2003. – Vol. 5, No. 1. – P. 41–48.

Graupe D. Identification of Systems / D. Graupe. – R. E. Krieger Publishing Company, 1976 p.

Огарков М. А. Методы статистического оценивания параметров случайных процессов / М. А. Огарков. – М.: Энергоатомиздат, 1990. – 207 с. [Ogarkov M. A. The Methods of the Statistical Estimation of the Random Process Parameters / M. A. Ogarkov. – Moscow, Energoatomizdat, 1990. – 207 p.] (In Russian).

Kesten H. Accelerated stochastic approximation / H. Kesten // Ann. Math. Stat.. – 1958. – No 29. – Р. 41–59.

Кліпа А. М. Визначення початкових значень параметрів для ідентифікації моделі в просторі станів / А. М. Кліпа, А. А. Тунік // Електроніка та системи управління. – 2008. – №4(18). – С. 104–109. [Klipa A. M. Determinition of initial parameter values for identification of state space model / A. M. Klipa, A.A. Tunik // Electronics and Control Systems. – 2008. – No 4 (18), – Р. 104–109.] (In Ukrainian).

Rauw M. The Flight Dynamics and Control Toolbox / M. Rauw. – MathWorks Company, 2000. – 263 p.


Full Text: PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.