A case study on neuro-fuzzy architectures applied to the system identification of a reduced scale fighter aircraft

Authors

  • Vitor Taha Sant'Ana
  • Roberto Mendes Finzi Neto

DOI:

https://doi.org/10.55905/oelv22n1-099

Keywords:

neuro-fuzzy, experimental flight data, machine learning, unsteady aerodynamic modeling

Abstract

This study investigates different architectures of Neuro-Fuzzy applied to unsteady aerodynamic modeling based on experimental data from a reduced-scale aircraft, known as Generic Future Fighter. The comparison is made considering different fuzzy inference methods, membership function shapes, number of membership functions to describe the input variables and different output functions, in the case of Takagi-Sugeno inference method. All these comparisons are made using the differential evolution as an optimization tool. In the end, the results present the best Neuro-Fuzzy configuration applied to the system identification of the GFF. Furthermore, the conclusion presents insights about the possible future implementation of the methodology.

References

Brandon, J. M.; Morelli, E. A., 2012. “Nonlinear aerodynamic modeling from flight data using advanced piloted maneuvers and fuzzy logic.”

Brandon, J. M.; Morelli, E. A., 2016. “Real-time onboard global nonlinear aerodynamic modeling from flight data.” Journal of Aircraft, American Institute of Aeronautics and Astronautics, v. 53, n. 5, p. 1261–1297.

Fossen, T.I., 2011. Handbook of marine craft hydrodynamics and motion control. John Wiley & Sons.

Jafelice, R.M., de Barros, L.C., Bassanezi, R.C. and Gomide, F., 2004. “Fuzzy modeling in symptomatic HIV virus infected population.” Bulletin of Mathematical Biology, 66, pp.1597-1620.

Jafelice, R.S.D.M., Barros, L.C.D. and Bassanezi, R.C., 2012. “Usando a teoria dos conjuntos fuzzy na modelagem de fenômenos biológicos.” In II Congresso Brasileiro de Sistemas Fuzzy, Natal-RN.

Jafelice, R. S. d. M. et al., 2003. “Modelagem fuzzy para dinamica de transferencia de soropositivos para hiv em doenca plenamente manifesta.” [sn].

Jang, J.-S., 1993. “Anfis: adaptive-network-based fuzzy inference system.” IEEE transactions on systems, man, and cybernetics, IEEE, v. 23, n. 3, p. 665–685.

Jouannet, C., Berry, P., Melin, T., Amadori, K., Lundström, D. and Staack, I., 2012. “Subscale flight testing used in conceptual design.” Aircraft Engineering and Aerospace Technology, 84(3), pp.192-199.

Li, K., Kou, J. and Zhang, W., 2022. “Deep learning for multifidelity aerodynamic distribution modeling from experimental and simulation data.” AIAA Journal, 60(7), pp.4413-4427.

Pereira, B.L., Jafelice, R.M. and Finzi, R.M., 2022. “An approach of pondered individual analysis method in aircraft control.” Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44(11), p.534.

Sobrón Rueda, A., 2021. “On Subscale Flight Testing: Cost-effective techniques for research and development”. Doctoral dissertation, Linköping University Electronic Press.

Sobron, A., Lundström, D., Staack, I. and Krus, P., 2016. “Design and testing of a low-cost flight control and data acquisition system for unstable subscale aircraft.” In 30th Congress of The International Council of the Aeronautical Sciences (ICAS), Daejeon, Korea, September 25-30, Daejeon, South Korea.. The International Council of the Aeronautical Sciences.

Storn, R. and Price, K., 1997. “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces.” Journal of global optimization, 11, pp.341-359.

Zan, B. W., Han, Z. H., Xu, C. Z., Liu, M. Q., & Wang, W. Z., 2022. “High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network.” Advances in Aerodynamics, 4(1), 1-31.

Published

2024-01-18

How to Cite

Sant’Ana, V. T., & Finzi Neto, R. M. (2024). A case study on neuro-fuzzy architectures applied to the system identification of a reduced scale fighter aircraft. OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA, 22(1), 1898–1919. https://doi.org/10.55905/oelv22n1-099

Issue

Section

Articles

Most read articles by the same author(s)