Autonomous Time-Optimal Many-Revolution Orbit Raising for Electric Propulsion GEO Satellites via Neural Networks

Abstract

Geostationary Earth orbit (GEO) satellites are of great significance in the space market. Low-thrust propulsion has been highly developed in the last decades because it is fuel-saving. Therefore, the design of GEO satellites is rapidly changing from classic high-thrust propulsion more and more toward low-thrust propulsion. However, the transfer time will be quite long using low-thrust propulsion and it will be very expensive if the ground supports the whole orbit raising. Therefore, autonomous orbit raising is necessary. Deep neural networks are trained to learn the optimal control. Results show that DNNs can be applied in this long-duration optimal control problem and have excellent performance.