Identification of aerodynamic coefficients using computational neural networks

Dennis J. Linse, Robert F. Stengel

Research output: Contribution to journalArticlepeer-review

137 Scopus citations


Precise, smooth aerodynamic models are required for implementing adaptive, nonlinear control strategies. Accurate representations of aerodynamic coefficients can be generated for the complete flight envelope by combining computational neural network models with an estimation-before-modeling paradigm for on-line training information. A novel method of incorporating first partial derivative information is employed to estimate the weights in individual feedforward neural networks for each aerodynamic coefficient. The method is demonstrated by generating a model of the normal force coefficient of a twin-jet transport aircraft from simulated flight data, and promising results are obtained.

Original languageEnglish (US)
Pages (from-to)1018-1025
Number of pages8
JournalJournal of Guidance, Control, and Dynamics
Issue number6
StatePublished - Nov 1993

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Space and Planetary Science


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