| Download | - View final version: A physics-based neural network for flight dynamics modelling and simulation (PDF, 1.6 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1186/s40323-022-00227-7 |
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| Author | Search for: Stachiw, Terrin1; Search for: Crain, Alexander1ORCID identifier: https://orcid.org/0000-0003-4961-8437; Search for: Ricciardi, Joseph1 |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Subject | flight simulation; modelling and simulation; aerospace |
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| Abstract | 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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| Publication date | 2022-07-04 |
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| Publisher | Springer |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | d8a83d58-2096-4ee7-97a6-7542edb05e72 |
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| Record created | 2023-09-21 |
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| Record modified | 2023-09-21 |
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