SavedAdd your journal

Journals / Havacılık ve Uzay Teknolojileri Dergisi / 2019 / Cilt: 12 - Sayı: 2

Aircraft Trim Analysis by Particle Swarm Optimization

Parçacık Sürü Optimizasyonu ile Uçak Trim Analizi

Altuğ İFTAR
İlkay GÜMÜŞBOĞA
Pages
185–196
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Simulation results confirm that the PSO based trim algorithm can find all the trim values with a high accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this work, a detailed nonlinear model for the full dynamics of the F-16 aircraft is first developed and coded in MATLAB.
📊
Key Findings & Data:In the developed algorithms, particle swarm optimization (PSO) method, which is a meta-heuristic method with a high convergence rate over a continuous search space, is used.
💡
Conclusion & Impact:Simulation results confirm that the PSO based trim algorithm can find all the trim values with a high accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this work, a detailed nonlinear model for the full dynamics of the F-16 aircraft is first developed and coded in MATLAB. This model includes the gravity model, variable atmospheric parameters, tabular aerodynamic functions, propulsion model, nonlinear control surface actuation models, and six degrees of freedom equations of motion. A numerical tool for computing all possible trim values, using the aforementioned model, is then developed. This tool can calculate the trim values at different operating points. In the developed algorithms, particle swarm optimization (PSO) method, which is a meta-heuristic method with a high convergence rate over a continuous search space, is used. Simulations around the calculated trim values are then made using the developed model. Simulation results confirm that the PSO based trim algorithm can find all the trim values with a high accuracy.

Özet

Bu çalışmada, F-16 uçağının tam dinamikleri için ayrıntılı bir doğrusal olmayan model ilk olarak MATLAB'da geliştirildi ve kodlandı. Bu model, yerçekimi modelini, değişken atmosferik parametreleri, tablo halinde aerodinamik fonksiyonları, itki modelini, doğrusal olmayan kontrol yüzeyi modellerini ve altı serbestlik dereceli hareket denklemlerini içerir. Yukarıda bahsedilen modeli kullanarak olası tüm trim değerlerini hesaplamak için nümerik bir hesaplama aracı geliştirilmiştir. Bu araç, farklı çalışma noktalarındaki trim değerlerini hesaplayabilir. Geliştirilen algoritmalarda, sürekli arama alanı üzerinden yüksek yakınsama oranına sahip meta-sezgisel bir yöntem olan parçacık sürüsü optimizasyonu (PSO) yöntemi kullanılmıştır. Hesaplanan trim değerleri etrafındaki açık-döngü simülasyonlar geliştirilen model kullanılarak koşturulur. Simülasyon sonuçları, PSO tabanlı trim algoritmasının tüm trim değerlerini yüksek hassasiyetle bulabileceğini göstermektedir.

🔖

Cite This Article

İFTAR, A., GÜMÜŞBOĞA, İ. (2022). Aircraft Trim Analysis by Particle Swarm Optimization. Havacılık ve Uzay Teknolojileri Dergisi, Cilt: 12 - Sayı: 2, 185–196. https://ajindex.com/articles/535203

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. B. Etkin and L. D. Reid, “Dynamics of Flight: Stability and Control,” New York: Wiley, 1996
  2. R. Gilmore, “Catastrophe Theory for Scientists and Engineers,” Courier Corporation, 1993
  3. N. Ananthkrishnan and K. S. Nandan, "Level Flight Trim and Stability Analysis Using Extended Bifurcation and Continuation Procedure," Journal of Guidance, Control, and Dynamics, Vol. 24, pp. 1225-1228, 2001
  4. A. Paranjape, N. Sinha and N. Ananthkrishnan, "Use of Bifurcation and Continuation Methods for Aircraft Trim And Stability Analysis: A State-Of-The-Art," Proceedings of the 45th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, pp. 1051-1069, 2007
  5. R. Enns and J. Si, "Helicopter Trimming and Tracking Control Using Direct Neural Dynamic Programming," IEEE Transactions on Neural Networks, Vol. 14, pp. 929-939, 2003
  6. J. Tupy and I. Zelinka, "Evolutionary Algorithms in Aircraft Trim Optimization," Proceedings of the 19th International Workshop on Database and Expert Systems Application, Turin, Italy, pp. 524-530, 2008
  7. W. Wang, D. Li and C. Liu, “Helicopter Flight Simulation Trim in the Coordinated Turn with the Hybrid Genetic Algorithm,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 233, No. 3, pp. 1159-1168, 2017
  8. J. Kennedy and R. Eberhart, "Particle Swarm Optimization,” Proceddings of IEEE International Conference on Neural Networks, Perth, WA, Australia, pp. 1942–1948, 1995
  9. M. Clerc, Particle Swarm Optimization. John Wiley & Sons, 2010
  10. G. Venter and J. Sobieszczanski-Sobieski, "Particle Swarm Optimization," AIAA Journal, Vol. 41, No. 8, pp. 1583-1589, 2003
  11. A. Andreeva-Mori, K. Kobayashi and M. Shindo, “Particle Swarm Optimization/Greedy-Search Algorithm for Helicopter Mission Assignment in Disaster Relief,” Journal of Aerospace Information Systems, Vol. 12, No. 10, pp. 646-660, 2015
  12. J. L. Foo, J. Knutzon, V. Kalivarapu, J. Oliver and E. Winer, “Path Planning of Unmanned Aerial Vehicles Using B-Splines and Particle Swarm Optimization,” Journal of Aerospace Computing, Information, and Communication, Vol. 6, No. 4, pp. 271-290, 2009
  13. R. J. Kenefic, “Finding Good Dubins Tours for Uavs Using Particle Swarm Optimization,” Journal of Aerospace Computing, Information, and Communication, Vol. 5, No. 2, pp. 47-56, 2008
  14. L. Sonneveldt, E. R. Van Oort, Q. P. Chu and J. A. Mulder, “Nonlinear Adaptive Trajectory Control Applied to an F-16 Model,” Journal of