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.
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ABSTRACT: We describe a novel adaptive non-linear model predictive controller which is based on the idea of neural-augmentation of reference el-ements, both at the level of the reduced model and at the level of the control action. The new methodology is primarily motivated by the desire to consistently incorporate existing legacy modeling and control techniques into an adaptive non-linear, yet real-time-capable, control framework. At the level of the model, a reference model is augmented using an adaptive neural element. Kalman filtering is used for identify-ing on-line the free parameters of the neural network, with the goal of maximizing the prediction fidelity of the model with respect to the plant. At the level of the control strategy, a reference solution is augmented using a second adaptive neural element. The augmenting control network is trained on-line for correcting the reference control action and promoting it to the solution of the underlying non-linear model predictive problem. The resulting neural-augmented control strategy is non-linear, yet it is real-time capable in the sense that it requires a fixed number of operations at each step. Furthermore, it substantially eases the adaption process, since the neural elements must only be trained to capture the defects of the reference legacy elements, which is small if such reference elements are adequate. The proposed procedures are demonstrated in a virtual environ-ment with the help of the classical model problem of the double inverted-pendulum, and with the more challenging reflexive control of an autonomous helicopter.01/1974;
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ABSTRACT: As the capabilities of computational fluid dynamics (CFD) to model full aircraft configurations improve, and the speeds of massively parallel machines increase, it is expected that CFD simulations will be used more and more to steer or in some cases even replace traditional flight test analyses. The mission of the US Air Force SEEK EAGLE office is to clear any new weapon configurations and loadings for operational use. As more complex weapons are developed and highly asymmetric loadings are requested, the SEEK EAGLE office is tasked with providing operational clearances for literally thousands of different flight configurations. High-fidelity CFD simulations employing the turbulent Navier–Stokes equations are in a prime position to help reduce some of the required wind-tunnel and/or flight test workload. However, these types of CFD simulations are still too time consuming to populate a full stability and control parameter database in a brute-force manner. This article reviews results previously published by the authors, which validate the ability of high-fidelity CFD techniques to compute static force and moment characteristics of aircraft configurations. A methodology to generate efficient but non-linear reduced-order aerodynamic loads models from dynamic CFD solutions, which in-turn may be used to quickly analyse various stability and control characteristics at a particular flight condition, is introduced, and the results based on the US Air Force F-16C fighter aircraft that exemplify the process are discussed.Aerospace Engineering. 01/2009;
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ABSTRACT: This paper presents some results of the flight test campaign conducted on the Tecnam P2006T aircraft, on the occasion of its certification process. This twin-engine propeller airplane is certified under the normal category CS-23 and FAR 23. A prototype of this light aircraft has been tested in flight for a post-design performance optimization and for the assessment of flight qualities. These experiences have led to the application of two winglets to the original wing. The final configuration has been extensively tested for the achievement of CS-23 certification. The longitudinal and lateral-directional response modes have been assessed and quantified. At the same time the longitudinal airplane model, through a dedicated set of flight maneuvers, has been characterized by means of parameter estimation studies. The aircraft stability derivatives have been estimated from the acquired flight data using the identification technique known as Output Error Method (OEM). Some estimated stability derivatives have been also compared with the corresponding values extracted from leveled flight tests and from wind tunnel tests performed on a scaled model of the aircraft.Aerospace Science and Technology 01/2011; 24(1):226-240. · 0.87 Impact Factor