An Unscented Kalman Filter–based iterative learning controller for multibody rotating scan optical spacecraft

Abstract

In this paper, to address the periodic control problem facing high-precision observation for multibody rotating scan optical spacecraft connected with active magnetic bearing (AMB), the Unscented Kalman Filter (UKF)–based iterative learning controller (UILC) is proposed. In the UILC, an adaptive backstepping controller (ABC) is deployed in the main loop to make the system state converge, and an UKF module is substituted for the memory module to use feedback information in real time and eliminate error propagation between adjacent periods. In the paper, the 9 degrees of freedom (DOF) error discrete dynamics are first derived to facilitate the convergence proved in the discrete domain. Then, the convergence analysis of the closed-loop system is investigated, and the state error supremum of closed-loop system in the mean square is indicated. Finally, for a comparison with the classical iterative learning controller (ILC), a series of numerical simulations are performed. As indicated by the simulation results, the UILC is capable of eliminating periodic deviation and error propagation during the adjacent periods.