NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712
The sustained increase of the air transportation sector over the last decades has led to traffic saturated situations, inducing higher costs for airlines and important negative impacts for airport surrounding communities. The efficient management of air traffic supposes that aircraft trajectories ar...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Instituto de Aeronáutica e Espaço (IAE)
2012-09-01
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| Series: | Journal of Aerospace Technology and Management |
| Subjects: | |
| Online Access: | https://www.jatm.com.br/jatm/article/view/152 |
| _version_ | 1828788533237645312 |
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| author | Wen-Chi Lu Walid El-Moudani Téo Cerqueira Revoredo Felix Mora-Camino |
| author_facet | Wen-Chi Lu Walid El-Moudani Téo Cerqueira Revoredo Felix Mora-Camino |
| author_sort | Wen-Chi Lu |
| collection | DOAJ |
| description | The sustained increase of the air transportation sector over the last decades has led to traffic saturated situations, inducing higher costs for airlines and important negative impacts for airport surrounding communities. The efficient management of air traffic supposes that aircraft trajectories are fully mastered and their impacts can be accurately forecasted. Inversion of aircraft flight dynamics, which are essentially nonlinear, appears necessary. Aircraft flight dynamics is shown to be differentially flat, which is a property that has enabled the development of new numerical tools for the management of complex nonlinear dynamic systems. However, since in the case of aircraft flight dynamics this differential flatness property is implicit, a neural network is introduced to deal with its numerical inversion. Results related to the developed neural network training are displayed, while potential uses of the proposed tool are discussed. |
| first_indexed | 2024-12-12T00:54:34Z |
| format | Article |
| id | doaj.art-b52c67875ba5444b904b294fe2398718 |
| institution | Directory Open Access Journal |
| issn | 2175-9146 |
| language | English |
| last_indexed | 2024-12-12T00:54:34Z |
| publishDate | 2012-09-01 |
| publisher | Instituto de Aeronáutica e Espaço (IAE) |
| record_format | Article |
| series | Journal of Aerospace Technology and Management |
| spelling | doaj.art-b52c67875ba5444b904b294fe23987182022-12-22T00:43:55ZengInstituto de Aeronáutica e Espaço (IAE)Journal of Aerospace Technology and Management2175-91462012-09-0142NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712Wen-Chi Lu0Walid El-Moudani1Téo Cerqueira Revoredo2Felix Mora-Camino3National Formosa University- Yunlin - TaiwanLebanese University, Faculty of Business Tripoli - LebanonUniversidade do Estado do Rio de Janeiro Rio de Janeiro/RJ - BrazilÉcole National de L'Aviation Civile Toulouse - FranceThe sustained increase of the air transportation sector over the last decades has led to traffic saturated situations, inducing higher costs for airlines and important negative impacts for airport surrounding communities. The efficient management of air traffic supposes that aircraft trajectories are fully mastered and their impacts can be accurately forecasted. Inversion of aircraft flight dynamics, which are essentially nonlinear, appears necessary. Aircraft flight dynamics is shown to be differentially flat, which is a property that has enabled the development of new numerical tools for the management of complex nonlinear dynamic systems. However, since in the case of aircraft flight dynamics this differential flatness property is implicit, a neural network is introduced to deal with its numerical inversion. Results related to the developed neural network training are displayed, while potential uses of the proposed tool are discussed.https://www.jatm.com.br/jatm/article/view/152Neural networksDifferential flatnessAircraft flight dynamics. |
| spellingShingle | Wen-Chi Lu Walid El-Moudani Téo Cerqueira Revoredo Felix Mora-Camino NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712 Journal of Aerospace Technology and Management Neural networks Differential flatness Aircraft flight dynamics. |
| title | NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712 |
| title_full | NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712 |
| title_fullStr | NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712 |
| title_full_unstemmed | NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712 |
| title_short | NEURAL NETWORKS MODELLING FOR AIRCRAFT FLIGHT GUIDANCE DYNAMICS DOI 10.5028/jatm.2012.04020712 |
| title_sort | neural networks modelling for aircraft flight guidance dynamics doi 10 5028 jatm 2012 04020712 |
| topic | Neural networks Differential flatness Aircraft flight dynamics. |
| url | https://www.jatm.com.br/jatm/article/view/152 |
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