Cessna Citation X Stall Characteristics Identification from Flight Data using Neural Networks

Ghazi, Georges; Bosne, Matthieu; Sammartano, Quentin; Botez, Ruxandra Mihaela

doi:10.2514/6.2017-0937

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…An alternate approach is to model the force and moment coefficients at a given timestep [49,50]. When the NN outputs force and moment coefficients, the corresponding derivatives at a given condition can be estimated by a method such as the "modified delta" approach used by Chauhan and Singh [49].…”

Section: Neural Networkmentioning

confidence: 99%

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

Stachiw

Crain

Ricciardi

2022

Preprint

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The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire flight envelope, the traditional models can be extended across the entire flight envelope by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire flight envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight.

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Section: Neural Networkmentioning

confidence: 99%

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

Stachiw

Crain

Ricciardi

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The NNs in literature often model output state derivatives such that they are analogous to a linearized state-space representation at a single flight condition. An alternate approach is to model the force and moment coefficients at a given timestep [50,51]. When the NN outputs force and moment coefficients, the corresponding derivatives at a given condition can be estimated by a method such as the "modified delta" approach used by Chauhan and Singh [50].…”

Section: Neural Networkmentioning

confidence: 99%

A physics-based neural network for flight dynamics modelling and simulation

Stachiw

Crain

Ricciardi

2022

Adv. Model. and Simul. in Eng. Sci.

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The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire operational envelope, the traditional models can be extended by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight.

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“…System identification in the time-domain can be implemented using several methods such as the equation error method (EEM) [3,8,9,[14][15][16][17], the output error method [8,9,14], the filter error method [8,18], and artificial neural networks [3,19,20]. Computational software tools such as FVSysID [8] and SIDPAC [9], running under MATLAB, are available for system identification [21].…”

Section: Introductionmentioning

confidence: 99%

Development of Aerodynamic and Propulsion Models Using the Iterative Equation Error Method

Millidere,

Akgül,

Leblebicioğlu

et al. 2023

Aerospace

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For developing high-fidelity flight simulations, an accurate and complete representation of the aerodynamic characteristics of the aircraft is necessary. To obtain a realistic aerodynamic database, system identification methods can be used to describe the applied forces and moments acting on the aircraft. This study is based on simulated flight test data from a nonlinear simulation of the F-16 aircraft. It is demonstrated that the complete set of aerodynamic coefficients can be reconstructed from the flight test data. Thrust forces and moments are obtained from ground tests. A practical system identification methodology based on the iterative equation error method to determine the nonlinear aerodynamic and engine thrust models in the absence of engine manufacturer data is developed. The estimated values obtained using the method are compared with the actual parameter values. A mathematical engine model that can be used to estimate the thrust force for any flight condition is also developed. The findings demonstrate that the proposed method yields accurate results. The developed methodology is well-suited for the identification of isolated aerodynamic drag and lift coefficients and the thrust model.

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Cessna Citation X Stall Characteristics Identification from Flight Data using Neural Networks

Cited by 12 publications

References 15 publications

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

A physics-based neural network for flight dynamics modelling and simulation

Development of Aerodynamic and Propulsion Models Using the Iterative Equation Error Method

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