Stability analysis and robustness assessment of deterministic and stochastic nonlinear moving horizon estimators

Abstract

This paper proposes a discussion on the classification of the formulations of nonlinear Moving Horizon Estimators (MHE) of the literature into two categories: deterministic and stochastic. The stability of the dynamics of the estimation error is discussed for the MHEs in both frameworks. This paper also provides full explicit formulation of the stability conditions for the MHE in the deterministic framework, which were not given in the literature. Furthermore, robustness of MHE in both frameworks with respect to model errors is investigated through a simulation example of space object tracking. Comparison with other more classical estimators such as EKF, UKF and particle filter is also achieved.

Adaptive master–slave unscented Kalman filter for grid voltage frequency estimation

“…When the process dynamics are well known and the process noise is adequately chosen, the EKF and the UKF produce smaller estimation errors than the PF [23]. Furthermore, the PF is very sensitive to the value of the process noise and the estimation errors increase [23]. For estimation problems where the non-linearities are described by abrupt changes, the EKF performs poorly and the UKF and the PF perform better.…”