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Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control resultsAs part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.
Document ID
19930013895
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Jani, Yashvant
(Togai InfraLogic, Inc. Houston, TX., United States)
Date Acquired
September 6, 2013
Publication Date
July 10, 1992
Subject Category
Cybernetics
Report/Patent Number
NAS 1.26:192291
NASA-CR-192291
Report Number: NAS 1.26:192291
Report Number: NASA-CR-192291
Accession Number
93N23084
Funding Number(s)
CONTRACT_GRANT: NCC9-16
PROJECT: RICIS PROJ. AR-06
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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