ATMOSPHERIC ENTRY

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.	64 views 26 downloads
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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