Artificial Neural Network Modeling of Damaged Aircraft

Abstract

Aircraft design and control techniques rely on the proper modeling of the aircraft's equations of motion. Many of the variables used in these equations are aerodynamic coefficients which are obtained from scale models in wind tunnel tests. In order to model damaged aircraft, every aerodynamic coefficient must be determined for every possible damage mechanism in every flight condition. Designing a controller for a damaged aircraft is particularly burdensome because knowledge of the effect of each damage mechanism on the model is required before the controller can be designed. Also, a monitoring system must be employed to decide when and how much damage has occurred in order to re configure the controller. Recent advances in artificial intelligence have made parallel distributed processors (artificial neural networks) feasible. Modeled on the human brain, the artificial neural network's strength lies in its ability to generalize from a given model. This thesis examines the robustness of the artificial neural network as a model for damaged aircraft.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1994
Accession Number
ADA283227

Entities

People

  • Clifford A. Brunger

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aeronautical Engineering
  • Aeronautics
  • Aircraft Design
  • Aircrafts
  • Artificial Intelligence
  • Computers
  • Equations
  • Equations Of Motion
  • Frequency Response
  • Models
  • Neural Networks
  • Parallel Processors
  • Scale Models
  • Transfer Functions
  • United States Naval Academy
  • Wind Tunnel Tests
  • Wind Tunnels

Readers

  • Aerodynamics/Aeronautics.
  • Auditory Neuroscience/Auditory Physiology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks