Satellite Orbits Guidance Using State Space Neural Network

Abstract

This paper develops a state-space neural network (SSNN) to predict the satellite thruster force and control osculating orbital elements during maneuvers. An adequate mathematical satellite model is implemented to simulate the satellite orbit trajectory. When using SSNN for control, the system identification adaptive neural network (ANN) model is implemented to represent the forward dynamics of the satellite. The prediction error between the implemented satellite model output and the ANN output is used as the ANN training's signal. In the system control stage the SSNN model is used to predict future satellite responses to potential control signals. neural predictive control (NPC) is basically a type of model-based predictive control, where the model for predictions is a neural network. Incorporating neural-network models in model based predictive control (MBPC) algorithms providing a NPC. The neural network for obtaining the predictions in the MBPC scheme is called state-space neural network (SSNN). State-space candidate models, which are likely to need less arguments than input-output models, this is clearly an advantage when small data sets only are available.