Robust fault and state-space estimation for linear uncertain systems: An RLS approach

This paper addresses the robust filtering problem of joint fault and state estimation for uncertain systems from the viewpoint of regularized least-square estimation. The method is based on the assumption that no prior knowledge about the dynamical evolution of the fault is available. Compared with earlier studies the robust criterion for least-square designs incorporate simultaneously both regularization and weighting and applies to a large class of uncertainties. The solution to the regularized least-square problem yields robust filter equations that perform regularization as opposed to de-regularization. The proposed filter is tested by an illustrative example.