Approximate reasoning-based learning and control for proximity operations and docking in spaceA recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.
Document ID
19910065124
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Berenji, Hamid R. (NASA Ames Research Center; Sterling Software, Inc. Moffett Field, CA, United States)
Jani, Yashvant (LinCom Corp. Houston, TX, United States)
Lea, Robert N. (NASA Johnson Space Center Houston, TX, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1991
Subject Category
Cybernetics
Report/Patent Number
AIAA PAPER 91-2803
Meeting Information
Meeting: AIAA Guidance, Navigation and Control Conference