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.
[Show abstract][Hide abstract] ABSTRACT: To ensure timely maintenance and efficient aircraft operations, it is necessary, to know and keep track of aircraft's actual performance. Flight performance is determined by aircraft's physical characteristics. Theoretical aircraft performance, obtained after manufacturing and flight testing, are described in flight manual. Transport aircraft in operation is usually exposed to standard operational conditions. Despite the standard operational conditions and regular aircraft maintenance, structure aging and high dynamic loads due to high subsonic Mach number could lead to changes of main physical factors that determine flight performance. For this reason actual aircraft performance often differs from theoretical. Commercial airlines monitor true performance of aircraft in operation. This paper presents overview of existing performance monitoring methods as well as first indications for new research possibilities regarding physical characteristics determination for aircraft in operation using flight data.
"One of the important aspects of flight-testing of an aircraft is the estimation of its stability and control parameter from flight data , . Parameter estimation is a key element of aircraft system identification, and system identification is a general procedure to match the observed input-output response of a system dynamics by a proper choice of an input output model and its physical parameters , . Aircraft parameter estimation has become an important tool for flight test engineers to determine the aerodynamic characteristic of an aircraft from the flight data . "
[Show abstract][Hide abstract] ABSTRACT: In this paper, application of neural networks combined with partial differentiation of the neural outputs has been discussed to estimate lateral-directional flight stability and control parameter. A neural model capable of predicting generalized force and moment coefficients using measured motion and control variables can be employed to extract aerodynamic parameters from flight data. The Neural Partial Differentiation method is used for this purpose. The estimated results are compared with the parameter estimates obtained from Output Error Method. The validity of estimates has been verified by the model validation method, wherein the estimated model response is matched with the flight-test data that are not used for estimating the parameter.
Cognitive Computing and Information Processing (CCIP), 2015 International Conference on; 04/2015
"The collected trajectory data and the aerodynamic model in (28) were used within the output error method detailed in  to estimate the 18 aerodynamic coefficients used to model the articulated MAV. The aerodynamic model was applied individually to each of the three lifting surface, and used to calculate aerodynamic forces and moments FAi, MAi, for i = 1 to 3 that appear in Figure 3 and the final MAV equations. "
[Show abstract][Hide abstract] ABSTRACT: Birds and insects naturally use passive flexing of their wings to augment their stability in uncertain aerodynamic environments. In a similar manner, micro air vehicle designers have been investigating using wing articulation to take advantage of this phenomenon. The result is a class of articulated micro air vehicles where artificial passive joints are designed into the lifting surfaces. In order to analyze how passive articulation affects performance of micro air vehicles in gusty environments, an efficient 8 degree-of-freedom model is developed. Experimental validation of the proposed mathematical model was accomplished using flight test data of an articulated micro air vehicle obtained from a high resolution indoor tracking facility. Analytical investigation of the gust alleviation properties of the articulated micro air vehicle model was carried out using simulations with varying crosswind gust magnitudes. Simulations show that passive articulation in micro air vehicles can increase their robustness to gusts within a range of joint compliance. It is also shown that if articulation joints are made too compliant that gust mitigation performance is degraded when compared to a rigid system.
The Scientific World Journal 01/2014; 2014:598523. DOI:10.1155/2014/598523 · 1.73 Impact Factor
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