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ATMOSPHERIC ENTRY

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Date Issued:
2017
Abstract/Description:
The development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and compared to the Apollo case.
Title: ATMOSPHERIC ENTRY.
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Name(s): Martin, Dillon A, Author
Elgohary, Tarek, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2017
Publisher: University of Central Florida
Language(s): English
Abstract/Description: The development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and compared to the Apollo case.
Identifier: CFH2000354 (IID), ucf:45874 (fedora)
Note(s): 2017-12-01
B.S.M.E.
College of Engineering and Computer Science, Mechancial and Aerospace Engineering
Bachelors
This record was generated from author submitted information.
Subject(s): Atmopheric Entry
Neural Networks
Reentry
Capsule
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFH2000354
Restrictions on Access: campus 2018-12-01
Host Institution: UCF

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