Adaptive Nonlinear Dynamic Inversion for Spacecraft Attitude Control with Fuel Sloshing

Abstract

Through the years researchers have developed many different forms of spacecraft attitude controllers ranging from simple linear controllers to highly nonlinear ones. For Nonlinear Dynamic Inversion controllers, the tracking performance depends on the model on the plant dynamics. In this paper we explore the response of a controlled satellite with liquid sloshing and apply neural networks to create an adaptive NDI controller. Feedforward neural networks are used to model any unknown system dynamics. The fuel motion is modeled using a mechanical model often used in the field of liquid sloshing. The equations of motion of the combined satellite/fuel system are derived and a simulation is constructed. Results in the form of tracking performance for both the standard and the adaptive NDI controller will be shown using a model of SloshSat, an experimental liquid sloshing satellite of ESA and the Netherlands Agency for Aerospace Programs. The results will demonstrate that an adaptive controller is indeed needed and that the proposed NDI controller with neural network is capable of excellent reference tracking in case of fuel sloshing.