Speed-adaptive dynamic surface attitude control for a satellite with moving masses under input constraints

Abstract

This paper investigates an attitude control technique for a low Earth orbit nanosatellite with moving masses based on the active use of aerodynamic forces. A speed-adaptive dynamic surface control scheme is designed to comprehensively solve the practical problems of aerodynamic model error, the dynamic effect of movement, stroke limitation, and slow convergence. Multiple constraints are imposed on the inputs to reduce the fast-varying dynamic effect of the masses to be negligible. Other slow-varying disturbances are precisely estimated by a nonlinear observer. In particular, to resolve the contradiction between the overshoot and the attitude convergence speed, a novel adaptive law is designed based on the smooth hyperbolic tangent function to adjust the convergence parameter within the given boundary. Moreover, considering the actual physical limitation, hard constraints are imposed on two actuators. Finally, by using the Lyapunov approach, it is proven that, despite uncertain dynamics, unknown disturbances and input constraints, the attitude error can be adjusted to be arbitrarily small by choosing the proper parameters. A semi-physical simulation platform is built to verify the feasibility of the moving mass actuator and the effectiveness and robustness of the proposed control scheme.

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

“…Considering the nonlinearity of the AMB system and coupled multibody dynamics, a suitable controller for multibody spacecraft is required. Some nonlinear control methods have been studied in the literature for the nonlinear and coupled system, such as robust control (Bai et al, 2019), high-precision tracking control (Riel et al, 2020), sliding-mode control (Chen et al, 2019; Yu and Xie, 2019), adaptive control (Hu et al, 2020; Wu et al, 2020a), backstepping control (Feng et al, 2020; Yu et al, 2014), and iterative learning control (Gao et al, 2013; Wang et al, 2013). These control methods are efficient in addressing imbalance and uncertainty problems.…”