State and disturbance estimation for test masses of drag-free satellites based on self-recurrent wavelet neural network

Abstract

In recent years, the drag-free satellites have been widely used for some fundamental physical experiments, such as checking short-range effects of general relativity, geopotential determination and the exploration of static ocean current. And the space-borne detector of gravitational waves is one of the important applications for drag-free satellites in the future. In this study, the estimation of relative motion state and disturbance for test masses of drag-free satellite after release are researched. Firstly, the relative motion model between the test masses is established based on corresponding reference frames. Secondly, a self-recurrent wavelet neural network estimator is designed to estimate the pure gravitational relative motion state and disturbance for the test masses, and the sliding mode controller is used to transfer the relative motion state to the ideal state. Finally, the proposed estimation methods are verified by the simulation results.