Moving Horizon Estimator with filtering and adaptive sampling

Oliva, F.; Carnevale, D.

doi:10.1016/j.ifacol.2022.09.044

IFAC-PapersOnLine

2022

DOI: 10.1016/j.ifacol.2022.09.044

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Moving Horizon Estimator with filtering and adaptive sampling

F. Oliva

D. Carnevale

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Cited by 2 publications

References 22 publications

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

Desai,

Oliva,

Ferrari

et al. 2023

IFAC-PapersOnLine

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

Desai,

Oliva,

Ferrari

et al. 2023

IFAC-PapersOnLine

View full text Add to dashboard Cite

Adaptive horizon size moving horizon estimation with unknown noise statistical properties

Wang,

Liu,

Yuan

et al. 2024

Meas. Sci. Technol.

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Moving horizon estimation (MHE) is an effective technique for state estimation. It formulates state estimation as an optimization problem over a finite time interval and is characterized by inherent robustness, flexibility, and explicit constraint handling capabilities. The horizon size is a crucial parameter influencing the estimation performance of MHE. However, the selection of the horizon size remains an open research question in the field of MHE. In this paper, we propose a novel adaptive horizon size MHE strategy that dynamically adjusts the horizon size based on the value of the objective function. This approach aims to improve the state estimation performance of MHE in real-time applications. Unlike conventional MHE methods that rely on a fixed horizon size, our adaptive strategy enhances robustness against unknown noise statistics by adjusting the horizon size. We analyze the convergence property of the estimation error and provide guidelines for parameter design to ensure optimal performance. The effectiveness and superiority of the proposed method are demonstrated through simulations involving an oscillatory system and a target tracking application under non-stationary noise conditions.

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Moving Horizon Estimator with filtering and adaptive sampling

Cited by 2 publications

References 22 publications

Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

Adaptive horizon size moving horizon estimation with unknown noise statistical properties

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