Flight Vehicle System Identification: A Time Domain MethodologyAmerican Institute of Aeronautics and Astronautics, 2006 - 534 pages This valuable volume offers a systematic approach to flight vehicle system identification and exhaustively covers the time domain methodology. It addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis. A pragmatic and balanced account of pros and cons in each case is provided. The book also presents data gathering and model validation, and covers both large-scale systems and high-fidelity modeling. Real world problems dealing with a variety of flight vehicle applications are addressed and solutions are provided. Examples encompass such problems as estimation of aerodynamics, stability, and control derivatives from flight data, flight path reconstruction, nonlinearities in control surface effectiveness, stall hysteresis, unstable aircraft, and other critical considerations. |
Table des matières
Model Characterization | 5 |
Estimation Techniques of the Past | 11 |
Chapter 2 | 33 |
Droits d'auteur | |
53 autres sections non affichées
Autres éditions - Tout afficher
Flight Vehicle System Identification: A Time Domain Methodology Ravindra V. Jategaonkar Affichage d'extraits - 2006 |
Expressions et termes fréquents
accelerations aerodynamic model aileron aircraft parameter algorithm analyzed angle of attack angular rates applied approach approximation basic Chapter coefficients computational control inputs convergence correlation cost function covariance matrix data points database deflection denote discussed Dornier 328 Dutch roll eigenvalues esti example extended Kalman filter FFNN filter error method flap flight data flight measured flight test flight vehicle system frequency Gauss-Newton method gradient ground effect independent variables initial conditions iteration Jategaonkar Journal of Aircraft Kalman filter likelihood function linear model linear systems longitudinal measurement noise necessary Neural Networks noise covariance nonlinear systems observation equations obtained optimization output error method parameter estimation perturbed pitch pitching moment predicted problem procedure process noise recursive regression response roll sideslip simulation step system identification system parameters techniques test_case Transall C-160 unknown parameters unstable aircraft update validation y(tk yields zero