Flight Vehicle System Identification: A Time Domain Methodology

DOI: 10.2514/4.866852 Publisher: AIAA, Reston, VA, USA, ISBN: 1-56347-836-6
Source: DLR


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.

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    • "where the hat symbol denotes the estimates, and the conditional probability p(z|Θ) is given by (3.5). As it is frequently done, we replace the probability maximization task by an easier action – minimization of a negative log likelihood function (Jategaonkar, 2006)into the negative log likelihood function and rejection of the constant terms allows one to obtain the cost function "
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    ABSTRACT: This paper is concerned with the designing of simultaneous flight control deflections for aircraft system identification. The elevator, ailerons and rudder are excited with harmonically related multisine signals. The optimal deflections are designed when there is no information about the stability and control derivatives and when this information is available. The inclusion of the system dynamics in the inputs design phase is done with the D-optimality criterion. Both sets of optimal flight surface deflections are used as excitations of a nonlinear aircraft model which is identified through the maximum likelihood estimation method. Parameters accuracy for those maneuvers (designed with and without a-priori knowledge) is presented and compared.
    Preview · Article · Jan 2016 · International Journal of Theoretical Physics
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    • "is: 1. Regression methods (multiple, linear, nonlinear) within equation error method (EEM), 2. Recursive regression methods (least squares, Fourier, etc.), 3. Neural network methods, 4. Maximum likelihood methods within output error method (OEM), and 5. Filtration methods (Kalman filter, Extended Kalman filter, etc.) within filter error method (FEM). Most used parameter estimation methods are: regression analysis (least squares method, LSM or total least squares method, TLSM) and maximum likelihood method (MLE) [14] [17] [19] "
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    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.
    Full-text · Article · Aug 2015 · Tehnicki Vjesnik
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    • "One of the important aspects of flight-testing of an aircraft is the estimation of its stability and control parameter from flight data [1], [2]. 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 [3], [4]. Aircraft parameter estimation has become an important tool for flight test engineers to determine the aerodynamic characteristic of an aircraft from the flight data [5]. "
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    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.
    Full-text · Conference Paper · Apr 2015
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