Date of Award
Master of Science in Engineering
Mechanical and Aerospace Engineering
Dr. Jennifer Hudson
Dr. Tianshu Liu
Dr. Brook Sullivan
Dr. Bob White
Genetic algorithm, observational orbit, satellite servicing, geostationary satellite, geostationary
Masters Thesis-Open Access
The problem of mission design for a robotic servicing satellite in geosynchronous Earth orbit (GEO) was investigated. A representative set of potential client satellites was selected, and operational needs were randomly assigned based on the average number of GEO retirements, anomalies, and repositioning maneuvers that currently occur each year. An objective function was developed to represent the value of servicing mission sequences, including client fees, time penalties, and operational risk. A genetic algorithm was then used to find sequences of operations on the potential client set that maximized the objective function’s value. Scenarios were analyzed with the database of satellites as well as with a dynamic client model. Sequences that begin with repair operations and later include refuel, observation, and retirement maneuvers were found to be the most valuable, with some differences in the optimal sequences depending on parameter values in the objective function. A second genetic algorithm was then developed to determine optimum firing patters for a satellite performing an observational relative orbit around another satellite. The associated objective function minimized the cost of metrics such as relative distance, illumination, observational coverage, and fuel consumption.
Verstraete, Andrew W., "Satellite Sequencing Optimization and Observational Orbit Determination Using Genetic Algorithms" (2017). Master's Theses. 906.