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Using Neural Networks in Decision Making for a Reconfigurable Electro Mechanical Actuator (EMA)The objectives of this project were to demonstrate applicability and advantages of a neural network approach for evaluating the performance of an electro-mechanical actuator (EMA). The EMA in question was intended for the X-37 Advanced Technology Vehicle. It will have redundant components for safety and reliability. The neural networks for this application are to monitor the operation of the redundant electronics that control the actuator in real time and decide on the operating configuration. The system we proposed consists of the actuator, sensors, control circuitry and dedicated (embedded) processors. The main purpose of the study was to develop suitable hardware and neural network capable of allowing real time reconfiguration decisions to be made. This approach was to be compared to other methods such as fuzzy logic and knowledge based systems considered for the same application. Over the course of the project a more general objective was the identification of the other neural network applications and the education of interested NASA personnel on the topic of Neural Networks.
Document ID
20010110022
Acquisition Source
Kennedy Space Center
Document Type
Other
Authors
Latino, Carl D.
(Oklahoma State Univ. Stillwater, OK United States)
Date Acquired
August 20, 2013
Publication Date
October 1, 2001
Publication Information
Publication: 2000 Research Reports: NASA/ASEE Summer Faculty Fellowship Program
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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