Proximity Moving Horizon Estimation for Discrete-Time Nonlinear Systems

Proximity Moving Horizon Estimation for Discrete-Time Nonlinear Systems

In this paper, 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...

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Bibliographic Details
Published in:2021 American Control Conference (ACC) pp. 328 - 333
Main Authors: Gharbi, Meriem, Bayer, Fabia, Ebenbauer, Christian
Format: Conference Proceeding
Language:English
Published: American Automatic Control Council 25-05-2021
Subjects:
Benchmark testing
Control theory
Estimation error
Filtering
Kalman filters
Nonlinear systems
Stability
Online Access:Get full text
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Summary:In this paper, 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.
ISSN:2378-5861
DOI:10.23919/ACC50511.2021.9483410