Robust learning observer-based actuator fault reconstruction for satellite attitude control systems

In this paper, robust actuator fault reconstruction for satellite attitude control systems (ACSs) in the presence of space environmental disturbances and actuator faults is investigated via a learning observer (LO). First, a nonlinear mathematical model of the satellite ACS satisfying Lipschitz constraint is established; then an LO is constructed to achieve robust reconstruction of satellite attitude angles, attitude angler velocities and actuator faults at the same time. Further, sufficient conditions for the robust stability of the LO are explicitly provided in this paper. The design problem of the proposed LO is formulated into a convex optimization problem in terms of linear matrix inequalities (LMIs) that can be directly solved using the LMI toolbox of MATLAB. At last, simulation studies on a satellite example are performed to illustrate the effectiveness and applicability of the presented LO-based fault-reconstructing strategy.