Particle Swarm Optimization Applied to Optimize Orbtial Decay and Re-boost Trajectories

Open Access
- Author:
- Chisholm, John Robert
- Area of Honors:
- Aerospace Engineering
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Robert Graham Melton, Thesis Supervisor
Robert Graham Melton, Thesis Honors Advisor
Dr. George A Lesieutre, Faculty Reader
David Bradley Spencer, Faculty Reader - Keywords:
- Oribtal Decay
Particle Swarm Optimization
PSO
Re-Boost Trajectory
Spacecraft Dynamics
Orbit Optimization - Abstract:
- Particle Swarm Optimization is a population-based stochastic method developed in recent years and successfully applied in several fields of research. Inspired by the behavior of bird flocks while searching for food, this method is meant to use information sharing to determine a global optimum solution. For this research, particle swarm optimization is used to optimize orbital decay and re-boost trajectories for spacecraft in Low-Earth orbits. The equations of motion for such a problem include solving ordinary differential equations, which model effects of gravity, low-density drag, and thrust accelerations. The thrust trajectory is optimized so that a spacecraft in circular orbit can re-boost back to its original orbit after decaying as a result of drag. The problem is solved using MATLAB and the code includes a series of nine functions which include the ode45 built-in numerical integrator. Three possible solutions are examined for the best solution: a single continuous thrust, a two-thrust maneuver that is split by a coasting arc, and a variable magnitude trust. Each method is further examined for possible sources of error. Ultimately, the two-thrust method is determined to be the most effective because it is the least error prone and used the least amount of propellant. Using the two thrust method, other spacecraft parameters are varied which include, the coefficient of drag, the spacecraft’s cross-sectional diameter, the spacecraft’s mass, the effective exhaust velocity of the thruster, and the thrust-to-mass ratio of the spacecraft. The starting and minimum altitude are also varied from as high as 300 km to as low as 150 km. Future work will include tracing sources of error in particle swarm optimization and its tendency to occasionally not find the global minimum as well as to correct some data inconsistencies in the variable thrust method.