Date of Award
Doctor of Philosophy
Mechanical and Aerospace Engineering
Dr. Jennifer S. Hudson
Dr. Richard M. Meyer
Dr. Kapsong Ro
Dr. Robert G. Trenary
Low-thrust, reinforcement learning, optimization, DOPG, spacecraft, astrodynamics
This dissertation explores a novel method of solving low-thrust spacecraft targeting problems using reinforcement learning. A reinforcement learning algorithm based on Deep Deterministic Policy Gradients was developed to solve low-thrust trajectory optimization problems. The algorithm consists of two neural networks, an actor network and a critic network. The actor approximates a thrust magnitude given the current spacecraft state expressed as a set of orbital elements. The critic network evaluates the action taken by the actor based on the state and action taken. Three different types of trajectory problems were solved, a generalized orbit change maneuver, a semimajor axis change maneuver, and an inclination change maneuver. When training the algorithm in a simulated space environment, it was able to solve both the generalized orbit change and semimajor axis change maneuvers with no prior knowledge of the environment’s dynamics. The robustness of the algorithm was tested on an inclination change maneuver with a randomized set of initial states. After training, the algorithm was able to successfully generalize and solve new inclination changes that it has not seen before.
This method has potential future applications in developing more complex low-thrust maneuvers or real-time autonomous spaceflight control.
Kolosa, Daniel S., "A Reinforcement Learning Approach to Spacecraft Trajectory Optimization" (2019). Dissertations. 3542.