Adaptive Neural Control for Space Structure Vibration Suppression.

Abstract

Despite recent advances in efficiency, current methodologies for space structure control design still engage significant human resources for engineering development and maintenance. The work reported in this document is part of an effort to develop neural network based controllers capable of self optimization, on line adaptation and autonomous fault detection and control recovery. Focusing on vibration suppression controllers, we reviewed previous work that experimentally demonstrated the application of a new neural network architecture to feedback control and feedforward control of tonal disturbances. New developments included efficient and completely autonomous neural network feedforward control for the case of broadband disturbances.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1996
Accession Number
ADB216141

Entities

People

  • David C. Hyland
  • Lawrence D. Davis

Organizations

  • Harris Corporation

Tags

Communities of Interest

  • Cyber
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Adaptive Control Systems
  • Closed Loop Systems
  • Computer Programming
  • Computer Programs
  • Computers
  • Control Systems
  • Control Systems Engineering
  • Detectors
  • Estimators
  • Filtration
  • Frequency Bands
  • Machine Learning
  • Manufacturing
  • Measurement
  • Operating Systems
  • Space Systems
  • Transducers

Readers

  • Acoustics.
  • Neural Network Machine Learning.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space
  • Space - Spacecraft Maneuvers