Full-Envelope Stitched Simulation Model of a Fighter Aircraft using the Lasso Technique

Millidere, Murat; Yigit, Tolga; Uslu, Samet

doi:10.2514/6.2022-1428

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Accordingly, the spline method is typically applied to models identified in the frequency-domain since spectral smoothing techniques allow the combination of several points about a flight condition to form a single frequency response for identification. A more recent publication from Millidere et al [38] proposes the Lasso technique as yet another means of determining the global model regressors. The downside of this method when compared to the proposed method is that it depends on first determining local linear models through Jacobian Linearisation.…”

Section: Model Structure Determination With Curve Fittingmentioning

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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Section: Model Structure Determination With Curve Fittingmentioning

confidence: 99%