Flight Vehicle System Identification: A Time Domain Methodology
ABSTRACT This valuable volume offers a systematic approach to flight vehicle system identification and covers exhaustively 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 are 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.
Beginners, as well as practicing researchers, engineers, and working professionals who wish to refresh or broaden their knowledge of flight vehicle system identification, will find this book highly beneficial. Based on years of experience, the book also provides recommendations for overcoming problems likely to be faced in developing complex nonlinear and high-fidelity models and can help the novice negotiate the challenges of developing highly accurate mathematical models and aerodynamic databases from experimental flight data.
Software that runs under MATLAB® and sample flight data are provided to assist the reader in reworking the examples presented in the text. The software can also be adapted to the reader’s own interests.
- SourceAvailable from: Ruxandra Mihaela Botez[Show abstract] [Hide abstract]
ABSTRACT: In the framework of this research project, the main rotor torque, tail rotor torque, engine torque and main rotor speed of a helicopter in forward flight are estimated by using a state space model from flight tests data. The state space model inputs are non-linear terms made of combinations of pilot controls and helicopter states. The model simulates the helicopter outputs while knowing the states and controls at all times. It was also implemented as a prediction tool, for possible use in an envelope protection flight control system in which the states, controls and outputs are known at the present time, and predict the future helicopter states and controls following to pilot controls time history. The state space model parameters are identified by using the subspace identification method, a relatively recent non-iterative algorithm which constructs an observability matrix from input and output data and uses this matrix to obtain the state-space matrices. The obtained parameters are then optimized with the Levenberg-Marquardt output-error 1 method. A comparison of the results with and without optimization is also conducted. The results show that the subspace method provides a good estimate of the outputs within the FAA tolerance bands and that these results can further be improved by use of the minimization algorithm. The generated model using the subspace method is found to be very good for prediction applications, which makes it a promising model for flight control simulator applications.Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering 01/2008; 222(G6):817-834. · 0.45 Impact Factor
- 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conferenc, Shaumburg, IL, USA; 04/2008
- 7th Australian Pacific Vertiflite Conference on Helicopter Technology, Melbourne, Australia; 03/2009