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

Download

Share

COinS
 

Accessibility Statement

This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.