Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data
Stochastic disturbances are everywhere. The influence of stochastic factors on the modeling and simulation of aircraft flight dynamics should be considered. Therefore, a stochastic differential equation for aircraft flight attitude is modeled based on the traditional one in this paper. After that, a...
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Format: | Article |
Language: | English |
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Wiley
2023-01-01
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Series: | International Journal of Aerospace Engineering |
Online Access: | http://dx.doi.org/10.1155/2023/5429395 |
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author | Haiquan Li Xiaoqian Chen Jiatu Zhang Bochen Wang Jiahui Peng Liang Wang |
author_facet | Haiquan Li Xiaoqian Chen Jiatu Zhang Bochen Wang Jiahui Peng Liang Wang |
author_sort | Haiquan Li |
collection | DOAJ |
description | Stochastic disturbances are everywhere. The influence of stochastic factors on the modeling and simulation of aircraft flight dynamics should be considered. Therefore, a stochastic differential equation for aircraft flight attitude is modeled based on the traditional one in this paper. After that, an identification method based on the idea of sparse recognition for unknown parameters and stochastic disturbance is proposed. Finally, a set of measured flight data is used to verify that the identified stochastic model has obvious advantages over the traditional deterministic model when the aircraft is maneuvering in flight. This method can improve the accuracy and reliability of the aircraft flight dynamic model. |
format | Article |
id | doaj-art-2878547fdde44284938cdb36d03271fd |
institution | Kabale University |
issn | 1687-5974 |
language | English |
publishDate | 2023-01-01 |
publisher | Wiley |
record_format | Article |
series | International Journal of Aerospace Engineering |
spelling | doaj-art-2878547fdde44284938cdb36d03271fd2025-02-03T06:43:09ZengWileyInternational Journal of Aerospace Engineering1687-59742023-01-01202310.1155/2023/5429395Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured DataHaiquan Li0Xiaoqian Chen1Jiatu Zhang2Bochen Wang3Jiahui Peng4Liang Wang5College of Aerospace Science and EngineeringNational Innovation Institute of Defense TechnologyDepartment of Applied Probability and StatisticsDepartment of Applied Probability and StatisticsDepartment of Applied Probability and StatisticsDepartment of Applied Probability and StatisticsStochastic disturbances are everywhere. The influence of stochastic factors on the modeling and simulation of aircraft flight dynamics should be considered. Therefore, a stochastic differential equation for aircraft flight attitude is modeled based on the traditional one in this paper. After that, an identification method based on the idea of sparse recognition for unknown parameters and stochastic disturbance is proposed. Finally, a set of measured flight data is used to verify that the identified stochastic model has obvious advantages over the traditional deterministic model when the aircraft is maneuvering in flight. This method can improve the accuracy and reliability of the aircraft flight dynamic model.http://dx.doi.org/10.1155/2023/5429395 |
spellingShingle | Haiquan Li Xiaoqian Chen Jiatu Zhang Bochen Wang Jiahui Peng Liang Wang Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data International Journal of Aerospace Engineering |
title | Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data |
title_full | Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data |
title_fullStr | Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data |
title_full_unstemmed | Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data |
title_short | Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data |
title_sort | identification of a stochastic dynamic model for aircraft flight attitude based on measured data |
url | http://dx.doi.org/10.1155/2023/5429395 |
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