Proximity Moving Horizon Estimation for Discrete-Time Nonlinear Systems

• Published in: IEEE control systems letters Vol. 5; no. 6; pp. 2090 - 2095
• Main Authors: Gharbi, Meriem, Bayer, Fabia, Ebenbauer, Christian
• Format: Journal Article
• Language: English
• Published: IEEE 01-12-2021
• Subjects: Asymptotic stability; Current measurement; Estimation; Estimation error; Kalman filtering; Kalman filters; Nonlinear systems; observers for nonlinear systems; Stability analysis; Time measurement
• ISSN: 2475-1456; 2475-1456
• DOI: 10.1109/LCSYS.2020.3046377

In this letter, we address the moving horizon estimation (MHE) problem of constrained discrete-time nonlinear systems. We propose a proximity-based formulation of the underlying optimization problem in which the state estimate is designed to lie in proximity of a stabilizing a priori estimate that is based on the extended Kalman filter (EKF). Using Lyapunov's direct method, we prove exponential stability of the estimation errors under suitable assumptions. For a benchmark example, an improved performance of the proposed estimator in comparison to the EKF and to a common MHE scheme with filtering update is showcased.