Distributed adaptive high‐gain extended Kalman filtering for nonlinear systems

Rashedi, Mohammad; Liu, Jingao; Huang, Biao

doi:10.1002/rnc.3838

Cited by 13 publications

(11 citation statements)

References 42 publications

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“…We first prove the case of l ≥ 2. Considering (2) and associated with the second term of system (25), one has…”

Section: Resultsmentioning

confidence: 99%

“…By means of properties (3) and (4) of the Kronecker product, the following expressions can be obtained 2] ) .…”

Section: System Modelmentioning

confidence: 99%

“…Due to the pervasive existence of nonlinearity in industrial systems, the nonlinear filtering problem has received tremendous research interest in the past few decades. Recently, a number of filtering algorithms have been proposed for nonlinear systems including extended Kalman filtering (EKF), [1][2][3] strong tracking filtering (STF), 4 unscented Kalman filtering (UKF), 5,6 and particle filtering. 7 For nonlinear systems with Gaussian noises, the EKF was developed by linearizing the nonlinear function at estimation of states.…”

Section: Introductionmentioning

confidence: 99%

“…7 For nonlinear systems with Gaussian noises, the EKF was developed by linearizing the nonlinear function at estimation of states. [1][2][3] However, EKF is not suitable for the nonlinear systems with parameter mismatches or high nonlinearities. Based on the orthogonal principle, the STF algorithm was designed in Reference 4 to offer strong robustness against the parameter mismatches of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

Sheng

Niu

Gao

et al. 2020

Intl J Robust & Nonlinear

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This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown-but-bounded disturbances, and state-and disturbance-dependent noises ((x, d)-dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input-to-state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum-of-squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.

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“…We first prove the case of l ≥ 2. Considering (2) and associated with the second term of system (25), one has…”

Section: Resultsmentioning

confidence: 99%

“…By means of properties (3) and (4) of the Kronecker product, the following expressions can be obtained 2] ) .…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

Sheng

Niu

Gao

et al. 2020

Intl J Robust & Nonlinear

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“…In (20), t 0 can be considered as a time instant when the filters communicate with each other. Proposition 1 implies that the upper bound of the prediction error of a predictor increases with time until the next communication time when the error is reset with the newly received information from other filters.…”

Section: Stability Analysismentioning

confidence: 99%

Communication delays and data losses in distributed adaptive high‐gain EKF

2016

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In this work, we consider distributed adaptive high‐gain extended Kalman filtering for nonlinear systems subject to data losses and delays in communications. Specifically, we consider a class of nonlinear systems that consist of several subsystems interacting with each other via their states. A local adaptive high‐gain extended Kalman filter is designed for each subsystem and the distributed estimators communicate to exchange the information. Each subsystem estimator takes the advantage of a predictor accounting for the delays and data losses simultaneously. The predictor of each subsystem is used to generate state predictions of interacting subsystems for interaction compensation. To get a reliable prediction, the predictors are designed based on a prediction‐update algorithm. The convergence of the proposed distributed state estimation is ensured under sufficient conditions handling communication delays and data losses. Finally, a chemical process example is used to evaluate the applicability and effectiveness of the proposed design. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4321–4333, 2016

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Partition‐based distributed extended Kalman filter for large‐scale nonlinear processes with application to chemical and wastewater treatment processes

Li,

Law,

Yin

2023

AIChE Journal

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In this article, we address a partition‐based distributed state estimation problem for large‐scale general nonlinear processes by proposing a Kalman‐based approach. First, we formulate a linear full‐information estimation design within a distributed framework as the basis for developing our approach. Second, the analytical solution to the local optimization problems associated with the formulated distributed full‐information design is established, in the form of a recursive distributed Kalman filter algorithm. Then, the linear distributed Kalman filter is extended to the nonlinear context by incorporating successive linearization of nonlinear subsystem models, and the proposed distributed extended Kalman filter approach is formulated. We conduct rigorous analysis and prove the stability of the estimation error dynamics provided by the proposed method for general nonlinear processes consisting of interconnected subsystems. A chemical process example is used to illustrate the effectiveness of the proposed method and to justify the validity of the theoretical findings. In addition, the proposed method is applied to a wastewater treatment process for estimating the full‐state of the process with 145 state variables.

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Distributed adaptive high‐gain extended Kalman filtering for nonlinear systems

Cited by 13 publications

References 42 publications

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

Communication delays and data losses in distributed adaptive high‐gain EKF

Partition‐based distributed extended Kalman filter for large‐scale nonlinear processes with application to chemical and wastewater treatment processes

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