Reinforcement learning framework for spacecraft low-thrust orbit raising
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The use of electric propulsion (EP) in satellites for transfer to geosynchronous equatorial orbit (GEO) is increasingly gaining importance among the space industry all around the world, and is proven a key for new space missions. In a conventional launch, the satellite is placed into a geostationary transfer orbit (GTO) by the launch vehicle and uses chemical propellants to reach GEO. This orbital transfer maneuver typically takes a few days. However, even though EP is far more e cient than the conventional chemical propulsion, its low thrust generation adds the complexity of longer transfer time from an equatorial orbit to GEO. This longer transit time leads to exposure of spacecraft to hazardous radiation of Van Allen belts. Therefore, there is a need to develop a method to determine the minimum transfer time trajectory for all-electric low thrust orbit raising problem. This thesis proposes a new formulation that facilitates the application of reinforcement learning to the problem of orbit raising. This work is based on the approach that the electric orbit-raising problem is posed as a sequence of multiple trajectory optimization sub-problems. Each sub-problem aims to move the spacecraft closest to GEO by minimizing a convex combination of suitably selected objectives. A mathematical formulation for the orbit-raising problem is proposed in the framework of reinforcement learning to enable adaptive modi cation of the objective function weights during a transfer. Due to high dimensionality of the planning states of the orbit-raising problem, arti cial neural networks are then constructed and trained on orbit-raising scenarios in order to compute the reward functions associated with reinforcement learning. The reward function for a planning state is de ned as the time required to reach GEO from that planning state. With the help of numerical simulations for planar and non-planar transfer scenarios, it is demonstrated that there is a reduction in transfer time for low-thrust orbit raising problem with the proposed methodology.
Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Aerospace Engineering