Accession Number : ADA232013


Title :   An Artificial Neural Network Control System for Spacecraft Attitude Stabilization


Descriptive Note : Master's thesis


Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA


Personal Author(s) : Segura, Clement M


Full Text : http://www.dtic.mil/dtic/tr/fulltext/u2/a232013.pdf


Report Date : Jun 1990


Pagination or Media Count : 77


Abstract : This document reports the results of research into the application of artificial neural networks to controlling dynamic systems. The network used is a feed-forward, fully-connected, 3-layer perception. Two methods of training neural networks via error back-propagation were used. Pattern matching training is a direct method that teaches the basic response. Performance index training is a new technique that refines the response. Performance index training is based on the concept of enforced performance. A neural network will learn to meet a specific performance goal if the performance standard is the only solution to a problem. Performance index training is devised to teach the neural network the time-optimal control law for the system. Real-time adaptation of a neural network in closed loop control of the Crew/Equipment Retriever was demonstrated in computer simulations.


Descriptors :   *COMPUTERIZED SIMULATION , NEURAL NETS , SPACECRAFT , ATTITUDE CONTROL SYSTEMS , TRAINING , REAL TIME , DYNAMICS , SOLUTIONS(GENERAL) , RESPONSE , CREWS , PATTERNS , ADAPTATION , MATCHING , CLOSED LOOP SYSTEMS , ATTITUDE(INCLINATION) , INDEXES(RATIOS) , CONTROL


Subject Categories : Psychology
      Manned Spacecraft


Distribution Statement : APPROVED FOR PUBLIC RELEASE