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.
In this paper, to realize the high-resolution observation mission, a new type of multibody optical spacecraft connected with active magnetic bearing (AMB) is introduced. Then, to address the attitude control problem facing high-precision observation for multibody spacecraft, the dynamic model of the multibody system is developed and an adaptive backstepping controller (ABC) is subsequently proposed. First, a high-precision electromagnetic force model of AMB is developed. Different from traditional models that only consider rotor position and current, the relative attitude between rotor and platform is considered. Then, based on the AMB model, the dynamic and kinematic model of multibody spacecraft is derived. Additionally, considering the electromagnetic bearing is unstable statically, an ABC method is proposed. The stability of the closed-loop system is guaranteed by the Lyapunov theorem. Finally, to indicate the effectiveness of the proposed method, some numerical simulations of comparison with the iterative learning control (ILC) method are performed. As indicated by the simulation results, the ABC is capable of eliminating periodic deviation, and it is more effective than the ILC in solving the control problem caused by periodic disturbance.
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