Neural network based dynamic model and gust identification system for the Jetstream G-NFLA

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dc.contributor.authorAntonakis, Aristeidis
dc.contributor.authorLone, Mudassir M.
dc.contributor.authorCooke, Alastair K.
dc.date.accessioned2016-08-11T14:15:45Z
dc.date.available2016-08-11T14:15:45Z
dc.date.issued2016-05-18
dc.description.abstractArtificial neural networks are an established technique for constructing non-linear models of multi-input-multi-output systems based on sets of observations. In terms of aerospace vehicle modelling, however, these are currently restricted to either unmanned applications or simulations, despite the fact that large amounts of flight data are typically recorded and kept for reasons of safety and maintenance. In this paper, a methodology for constructing practical models of aerospace vehicles based on available flight data recordings from the vehicles’ operational use is proposed and applied on the Jetstream G-NFLA aircraft. This includes a data analysis procedure to assess the suitability of the available flight databases and a neural network based approach for modelling. In this context, a database of recorded landings of the Jetstream G-NFLA, normally kept as part of a routine maintenance procedure, is used to form training datasets for two separate applications. A neural network based longitudinal dynamic model and gust identification system are constructed and tested against real flight data. Results indicate that in both cases, the resulting models’ predictions achieve a level of accuracy that allows them to be used as a basis for practical real-world applications.en_UK
dc.identifier.citationAntonakis, A., Lone, M. M., Cooke, A. K. (2016) Neural network based dynamic model and gust identification system for the Jetstream G-NFLA, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol 231, Issue 6, 2017, pp1138-1153en_UK
dc.identifier.issn0954-4100
dc.identifier.urihttp://dx.doi.org/10.1177/0954410016648997
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10277
dc.language.isoenen_UK
dc.publisherSageen_UK
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectArtificial neural networksen_UK
dc.subjectFlight testingen_UK
dc.subjectSystem identificationen_UK
dc.subjectGust identificationen_UK
dc.subjectHybrid identificationen_UK
dc.titleNeural network based dynamic model and gust identification system for the Jetstream G-NFLAen_UK
dc.typeArticleen_UK

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