Applications of an evolutionary strategy on general perturbation equations for optimization of low-thrust near-Earth orbit transfers

With recent developments in low-thrust optimization techniques, several methods of trajectory optimization can be implemented across various transfers. Much recent work has focused on performing methods of optimal control on low-thrust, near-Earth orbit transfers, achieving maximum efficiency in both energy use and time-of-flight. However, the use of optimal control relies on the exploitation of a satellites' equation of state, which becomes problematic if optimization is to be performed through a venue where the state equations cannot be manipulated. This situation is particularly evident when attempting to perform trajectory optimization through a commercial off-the-shelf satellite mission modeling software package. Thus, a robust optimization method must be chosen to produce competitive results comparable to optimal control in these types of situations. However, the formulation of an objective function, as well as which type of optimization method to choose is important, since some for-mulations or algorithms may lead to better or faster convergence when compared to others. Previous work has been devoted to assessing which optimization algorithms work best within a "black box" commercial software package, in which an evolutionary strategy was found to be the most robust. In this study, a near-Earth low-thrust trajectory will be modeled in Satellite Toolkit's Astrogator® with an evolutionary strategy applied. An appropriate objective function, and problem formulation in STK based on general perturbation equations is optimized within this evolutionary strategy, in an attempt to create a optimization method which can produce results on par with those found using a method of optimal control.