Deep learning for deorbiting control of an electrodynamic tether system

Abstract

This paper studies the nonlinear dynamics and deorbiting control of an electrodynamic tether (EDT) system in elliptical orbits. The orbital dynamics of the system under Lorentz forces is modelled using a set of modified equinoctial elements, whereas the attitude dynamics of the system is modelled based on the dumbbell assumption. A deep learning scheme of nonlinear model predictive control (NMPC) schemes is proposed for deorbiting control of the EDT system. To facilitate controller design, a time-scale separation method is utilized to simplify the attitude dynamics of the EDT system. A deep learning-based NMPC control law is developed with two stages. In the first stage, a large amount of data is generated using a conventional NMPC law to construct a dataset for training a deep neural network (DNN). In the second stage, the trained DNN is used to realize the real-time mapping from the system state to the system control, and thereby the feedback control of the system deorbit is achieved with extremely low computational cost. Finally, the efficacy and performance of the proposed deep learning-based NMPC control law are demonstrated via numerical case studies.