Identification of Aerodynamic Coefficients Using Computational Neural Networks

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 09, 1992
Accession Number
ADA244711

Entities

People

  • Dennis J. Linse
  • Robert F. Stengel

Organizations

  • Princeton University

Tags

Communities of Interest

  • Air Platforms
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Aircrafts
  • Algorithms
  • Equations
  • Equations Of Motion
  • Equations Of State
  • Estimators
  • Information Science
  • Jet Transport Aircraft
  • Kalman Filters
  • Maximum Likelihood Estimation
  • Neural Networks
  • Random Variables
  • Systems Management
  • Training
  • Transport Aircraft

Readers

  • Aerodynamics/Aeronautics.
  • Aviation Science / Aeronautics.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks