Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure
Nonlinear aerodynamics complexities of trending supersonic fighter aircraft entail formulation of a robust and reliable System Identification (Sys ID) technique that is capable of giving deep insight into its nonlinear characteristics and being self-capable of fitting into future advancements. This...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825005551 |
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| author | Muhammad Fawad Mazhar Muhammad Wasim Manzar Abbas Imran Shafi Jamshed Riaz Tae-hoon Kim Imran Ashraf |
| author_facet | Muhammad Fawad Mazhar Muhammad Wasim Manzar Abbas Imran Shafi Jamshed Riaz Tae-hoon Kim Imran Ashraf |
| author_sort | Muhammad Fawad Mazhar |
| collection | DOAJ |
| description | Nonlinear aerodynamics complexities of trending supersonic fighter aircraft entail formulation of a robust and reliable System Identification (Sys ID) technique that is capable of giving deep insight into its nonlinear characteristics and being self-capable of fitting into future advancements. This study discovers a decoupled longitudinal aerodynamic model of an open-loop supersonic aircraft using a novel algorithm that blends grey-box modeling architecture i.e. Box–Jenkins (BJ) structure with Bayesian approach, named as Box–Jenkins–Bayesian–Estimation (BJBE). BJ model utilizes a nonlinear least square estimator for parameter identification, which has been improved by the Levenberg–Marquardt algorithm for parameter error minimization, and further refinement is accomplished through Bayes’ theorem using its maximum-a-posteriori characteristics. Bayesian estimation, due to its a-priori feature, fully explores grey-box modeling BJ structure, which no other estimation technique does. The proposed solution involves the construction of a discrete-time BJ model using a simulated input–output dataset generated from the Flight Dynamic Model of F-16 aircraft, followed by the reduced-order model using Bayesian information criteria and parameter optimization using Bayesian theorem. A closer analysis of results has been conducted through statistical techniques like residual analysis, best-fit percentage, fit percentage error, mean squared error, and model order. Results show good agreement between model predictions and simulated flight data with an accuracy of 82.42%. Based upon this research, control laws of supersonic jets have been investigated through a novel technique, further leading to the development of its flight simulator module. |
| format | Article |
| id | doaj-art-91192ef0e6044a74a12c55f8f4ab0a7e |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-91192ef0e6044a74a12c55f8f4ab0a7e2025-08-22T04:55:14ZengElsevierAlexandria Engineering Journal1110-01682025-08-0112747248510.1016/j.aej.2025.04.059Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structureMuhammad Fawad Mazhar0Muhammad Wasim1Manzar Abbas2Imran Shafi3Jamshed Riaz4Tae-hoon Kim5Imran Ashraf6Department of Aeronautics and Astronautics, Institute of Space Technology, Islamabad, PakistanDepartment of Aeronautics and Astronautics, Institute of Space Technology, Islamabad, PakistanAir University, Islamabad, PakistanCollege of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Aeronautics and Astronautics, Institute of Space Technology, Islamabad, PakistanSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, No. 318, Hangzhou, Zhejiang, China; Corresponding authors.Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, South Korea; Corresponding authors.Nonlinear aerodynamics complexities of trending supersonic fighter aircraft entail formulation of a robust and reliable System Identification (Sys ID) technique that is capable of giving deep insight into its nonlinear characteristics and being self-capable of fitting into future advancements. This study discovers a decoupled longitudinal aerodynamic model of an open-loop supersonic aircraft using a novel algorithm that blends grey-box modeling architecture i.e. Box–Jenkins (BJ) structure with Bayesian approach, named as Box–Jenkins–Bayesian–Estimation (BJBE). BJ model utilizes a nonlinear least square estimator for parameter identification, which has been improved by the Levenberg–Marquardt algorithm for parameter error minimization, and further refinement is accomplished through Bayes’ theorem using its maximum-a-posteriori characteristics. Bayesian estimation, due to its a-priori feature, fully explores grey-box modeling BJ structure, which no other estimation technique does. The proposed solution involves the construction of a discrete-time BJ model using a simulated input–output dataset generated from the Flight Dynamic Model of F-16 aircraft, followed by the reduced-order model using Bayesian information criteria and parameter optimization using Bayesian theorem. A closer analysis of results has been conducted through statistical techniques like residual analysis, best-fit percentage, fit percentage error, mean squared error, and model order. Results show good agreement between model predictions and simulated flight data with an accuracy of 82.42%. Based upon this research, control laws of supersonic jets have been investigated through a novel technique, further leading to the development of its flight simulator module.http://www.sciencedirect.com/science/article/pii/S1110016825005551Supersonic aircraftSystem identificationAerodynamic modelNonlinear parameter estimationBox–Jenkins structure |
| spellingShingle | Muhammad Fawad Mazhar Muhammad Wasim Manzar Abbas Imran Shafi Jamshed Riaz Tae-hoon Kim Imran Ashraf Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure Alexandria Engineering Journal Supersonic aircraft System identification Aerodynamic model Nonlinear parameter estimation Box–Jenkins structure |
| title | Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure |
| title_full | Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure |
| title_fullStr | Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure |
| title_full_unstemmed | Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure |
| title_short | Aerodynamic model identification of supersonic aircraft using Bayesian approach-based Box–Jenkins structure |
| title_sort | aerodynamic model identification of supersonic aircraft using bayesian approach based box jenkins structure |
| topic | Supersonic aircraft System identification Aerodynamic model Nonlinear parameter estimation Box–Jenkins structure |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825005551 |
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