Moving horizon estimation for constrained system with model uncertainties

A robust moving horizon state estimation algorithm for constrained system with uncertainties in the state matrix is proposed. The state estimation problem of the system is described as Min-Max problem when the polytopic uncertainty exists in the state matrix, and estimation error variance upper bound is given as considering the polytopic uncertainty in the state matrix. The sufficient conditions of estimator for existence can be obtained in terms of linear matrix inequalities (LMIs). Based on the model predictive control(MPC) algorithm, a kind of robust optimal state estimation method on the worst case can be obtained by solving objective function with constraints considered. Eventually, the effectiveness of the proposed algorithm is verified by simulation.