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

Li, Xiaojie; Law, Adrian Wing‐Keung; Yin, Xunyuan

doi:10.1002/aic.18229

Cited by 9 publications

(1 citation statement)

References 39 publications

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“…where P i,k is updated using the covariance matrix update formula of distributed Kalman filter proposed in Li et al 48 L i,k ¼ ðCA ½:…”

Section: Centralized Mhementioning

confidence: 99%

Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes

Li,

Bo,

Zhang

et al. 2023

AIChE Journal

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In this article, we consider a state estimation problem for large‐scale nonlinear processes in the absence of first‐principles process models. By exploiting process operation data, both process modeling and state estimation design are addressed within a distributed framework. By leveraging the Koopman operator concept, a parallel subsystem modeling approach is proposed to establish interactive linear subsystem process models in higher‐dimensional subspaces, each of which correlates with the original nonlinear subspace of the corresponding process subsystem via a nonlinear mapping. The data‐driven linear subsystem models can be used to collaboratively characterize and predict the dynamical behaviors of the entire nonlinear process. Based on the established subsystem models, local state estimators that can explicitly handle process operation constraints are designed using moving horizon estimation. The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner. The proposed framework is applied to a chemical process and an agro‐hydrological process to illustrate its effectiveness and applicability. Good open‐loop predictability of the linear subsystem models is confirmed, and accurate estimates of the process states are obtained without requiring a first‐principles process model.

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“…where P i,k is updated using the covariance matrix update formula of distributed Kalman filter proposed in Li et al 48 L i,k ¼ ðCA ½:…”

Section: Centralized Mhementioning

confidence: 99%

Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes

Li,

Bo,

Zhang

et al. 2023

AIChE Journal

Self Cite

View full text Add to dashboard Cite

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Event‐Triggered Distributed Resilient State Estimator for Nonlinear Cyber‐Physical Systems Under Hybrid Cyberattacks

Wu,

Zhang

2024

Adaptive Control & Signal

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This article investigates the problem of event‐triggered distributed resilient state estimator (ET‐DRSE) for nonlinear cyber‐physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattacks is constructed by considering both false data injection (FDI) and Denial‐of‐Service (DoS) attacks. By proposing an event‐triggered mechanism, the network burden is reduced by decreasing the transmission rate of measurement information. To improve the flexibility, a detector‐based distributed resilient state estimator framework is derived. Coupled Riccati‐like difference equations (CRDEs) are proposed to obtain an upper bound (UB) of the estimation error covariance (EEC) of every subsystem. An estimation gain is designed to minimize the UB upon the error covariance of the resilient estimator by recursively solving the CRDEs. It is proved that the estimation error is probabilistically bounded through the application of random analytic theory. Finally, the performance of the proposed ET‐DRSE is verified through a numerical example and a chemical process example.

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Self‐tuning moving horizon estimation of nonlinear systems via physics‐informed machine learning Koopman modeling

Yan,

Han,

Law

et al. 2024

AIChE Journal

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In this article, we propose a physics‐informed learning‐based Koopman modeling approach and present a Koopman‐based self‐tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks—the state lifting network and the noise characterization network—using both data and available physical information. The first network accounts for the nonlinear lifting functions for the Koopman model, while the second network characterizes the system noise distributions. Accordingly, a stochastic linear Koopman model is established in the lifted space to forecast the dynamic behaviors of the nonlinear system. Based on the Koopman model, a self‐tuning linear moving horizon estimation (MHE) scheme is developed. The weighting matrices of the MHE design are updated using the pretrained noise characterization network at each sampling instant. The proposed estimation scheme is computationally efficient, as only convex optimization needs to be solved during online implementation, and updating the weighting matrices of the MHE scheme does not require re‐training the neural networks. We verify the effectiveness and evaluate the performance of the proposed method via the application to a simulated chemical process.

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

Cited by 9 publications

References 39 publications

Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes

Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes

Event‐Triggered Distributed Resilient State Estimator for Nonlinear Cyber‐Physical Systems Under Hybrid Cyberattacks

Self‐tuning moving horizon estimation of nonlinear systems via physics‐informed machine learning Koopman modeling

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