An Application of Artificial Intelligence Theory to Reconfigurable Flight Control.

Abstract

A knowledge-based flight control system capable of detecting, identifying, and reconfiguring for a wide range of aircraft failures has been designed. Combining modern control theory, statistical hypothesis testing, and artificial intelligence techniques, this research addresses the question of whether or not an intelligent computer could assist a pilot during a failure. Analytical redundancy techniques, including a Generalized Likelihood test, are used for failure detection. Failure diagnosis is performed by an expert system. Utilizing knowledge of cause-and-effect relationships between all aircraft components and the statistical results of a Multiple-Model algorithm, the expert system decides which aircraft component has failed and how to reconfigure for the failure. Preliminary tests on an 8-bit microprocessor system were conducted and are summarized, and plans to expand to a 16-bit multi-microprocessor system are outlined. Keywords: Cybernetics; Systems analysis; FDIR (Aircraft, Failure Detection Identification and Reconfiguration). (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 30, 1984
Accession Number
ADA165082

Entities

People

  • David A. Handelman

Organizations

  • Princeton University

Tags

Communities of Interest

  • Air Platforms
  • Sensors

DTIC Thesaurus Topics

  • Aircraft Equipment
  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Control Systems
  • Control Theory
  • Damage Detection
  • Detection
  • Detectors
  • Expert Systems
  • Failure Mode And Effect Analysis
  • Flight Control Systems
  • Mathematical Models

Fields of Study

  • Engineering

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Regression Analysis.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference