Abstract
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.
Thesis Completion
2017
Semester
Fall
Thesis Chair/Advisor
Elgohary, Tarek
Degree
Bachelor of Science in Mechanical Engineering (B.S.M.E.)
College
College of Engineering and Computer Science
Department
Mechanical and Aerospace Engineering
Location
Orlando (Main) Campus
Language
English
Access Status
Open Access
Length of Campus-only Access
1 year
Release Date
6-1-2019
Recommended Citation
Martin, Dillon A., "Atmospheric Entry" (2017). Honors Undergraduate Theses. 354.
https://stars.library.ucf.edu/honorstheses/354