HARDWARE AND SOFTWARE MEANS FOR DYNAMIC RECONFIGURATION OF A GROUP OF SMALL SPACE VEHICLES
Abstract
The goal of the study is to automate the control of a group of nanosatellites in conditions of its variable number by updating its state based on sending and processing broadcast requests between nanosatellites and using the Transformer neural network. A neural network is needed to make predi ctions about the state of the spacecraft network. The problem of ensuring connectivity of a network of nanosatellites is studied, which comes down to the implementation of adaptive network control with assessment and prediction of the state of communication channels between pairs of devices based on a neural network. Dynamic reconfiguration and machine learning of a network of devices have been developed. Algorithmic tools have been defined for the initial training of a neural network and its subs equent additional training, taking into account the preprocessing of the original sparse or fully connected data sets about the network of devices. Upon completion of training on synthetic data, the created neural network is able to predict the quality of communication, taking into account line of sight, signal attenuation depending on distance and the state of the nanosatellite hardware platform. The developed software system performs deterministic reconfiguration based on the current state of the nanosatellite network and adaptive reconfiguration based on historical data by analyzing the hidden patterns of nanosatellite functioning using the Transformer neural network. To predict the quality of communication, a functional is used to connect the geodetic coordinates of pairs of satellites and the vectors of their states with the elements of the matrix of the quality of communication between nanosatellites with a given initial time, the value of the time interval, and the value of the sampling step of the measurement process. The use of neural networks implemented on GPUs made it possible to predict possible states of nanosatellites and carry out reconfiguration of the constellation ahead of schedule, including removing “problematic” nanosatellites from the network.
