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.

A Secondary Particle Filter Photometric Data Inversion Method of Space Object Characteristics

“…The inversion method based on model construction improves the inversion accuracy by establishing a more complex model. This method quantitatively estimates the attitude, angular velocity, surface material and other SOC through the established shape model or photometric data model of the space object, such as the inversion methods based on two-panel model [8][9][10][11], closed facet model [12,13] and nonlinear filtering [14][15][16][17][18][19][20][21][22][23][24][25].…”

“…Unscented Kalman filter (UKF) [24] and particle filter (PF) [25] are the main nonlinear filtering methods currently used for SOC inversion. As to particle filter, particle degradation is a prominent problem, efforts must be made to obtain better efficiency and effectiveness in state estimation [26].…”

Dynamic ILC for Linear Repetitive Processes Based on Different Relative Degrees

“…The authors of [22,23] used 2D system theory to design the controller, but the stability analysis and applicable cases are not completely given. In [24,25], a dynamic filter is designed; however, it is difficult to control the system stability for uncertain systems. For the system with an uncertain ILC model, a dynamic filter was designed to make the system error converge in [26,27], while both of these papers did not consider the system fault.…”