DETERMINATION OF THE FORCE IMPACT OF AN ION THRUSTER PLUME ON AN ORBITAL OBJECT VIA DEEP LEARNING

The subject of research is the process of creating a neural network model (NNM) for determining the force impact of an ion thruster (IT) plume on an orbital object during non-contact space debris removal. The work aims to develop NNMs and study the influence of various factors on the accuracy of determining the force transmitted by the ion plume of the thruster to a space debris object (SDO). The tasks to resolve are to choose the structures of the NNMs, form a data set and use this data to train and validate the NNMs, and to explore the influence of the model structure and optimizer parameters on the accuracy of force determination. The methods used are plasma physics, computer simulation, deep learning, and optimization using an improved version of stochastic gradient descent. As a result of research, three NNMs have been developed, which differ in the number of hidden layers and neurons in hidden layers. For training and validation of the NNMs, a data set was generated for an SDO approximated by a cylinder using an autosimilar description of the ion plasma propagation. The data set was obtained for various relative positions and orientations of the object in the process of its removal from an orbit. Using this data set, the NNM parameters were optimized with the supervised learning method. The optimizer and its parameters are selected, providing a small error at the stage of validating learning outcomes. It was found that the accuracy of determining the force depends on the relative position and orientation of the SDO, as well as the architecture of the NNM, and the features of this influence were identified. The approach applied allows us to obtain the possibility of using methods of deep learning to determine the force impact of the IT plume on the SDO. The proposed models provide the accuracy of the force impact determination, which is sufficient for solving the considered class of problems. At the same time, NNM makes it possible to obtain results much faster in comparison with the methods used previously. This fact makes the NNMs promising to use both on-board and in mathematical modeling of missions to remove space debris.