In-orbit system identification of a flexible satellite with variable mass using dual Unscented Kalman filters

Modern space mission concepts are increasingly dependent on the robust and reliable deployment of spacecraft with large appendages, such as antennas, booms or solar panels. Such deployment requires the ability to properly capture and control the coupled system dynamics, which requires accurate in-orbit system identification of the mass and structural properties. This paper utilises dual Unscented Kalman filters (DUKF) to develop an online system identification strategy that captures both the structural and mass properties, and the attitude and orbit dynamics. The dynamics of the flexible multibody problem are derived from the Lagrangian equations, with the flexible body characteristics modelled with finite element software. A genetic algorithm is used to optimise the accelerometer placement, and hence improve the DUKF performance. We demonstrate that this approach can accurately capture the coupled attitude, orbit, and structural dynamics, as well as being able to provide in-orbit updates for mass properties such as the moment of inertia. The methodology is explored for two illustrative cases: one in which the initial moment of inertia is incorrectly characterised, one in which the moment of inertia changes with time. In both cases, the DUKF approach captures both the system dynamics and the mass properties, which are captured with an error of less than 1%.