Robust moving horizon state estimation for nonlinear systems

In this work, a robust moving horizon estimation scheme augmented by an auxiliary nonlinear observer is proposed for nonlinear systems with bounded model uncertainties. Specifically, an auxiliary deterministic nonlinear observer that asymptotically tracks nominal system state is taken advantage of to calculate a confidence region that contains the actual system state taking into account the effects of bounded model uncertainties at every sampling time. This region is then used to design a constraint on the state estimates in the proposed moving horizon estimation. The proposed design brings together deterministic and optimization-based observer design techniques. First, the proposed moving horizon estimation scheme is proved to give bounded estimation errors in the case of bounded model uncertainties. Second, the proposed approach provides another option to compromise the effects of errors in arrival cost approximations and can be used together with different arrival cost approximation techniques to further improve the state estimate.