Data-driven moving horizon state estimation of nonlinear processes using Koopman operator

In this paper, a data-driven constrained state estimation method is proposed for nonlinear processes. Within the Koopman operator framework, we propose a data-driven model identification procedure for state estimation based on the algorithm of extended dynamic mode decomposition, which seeks an opti...

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Main Authors: Yin, Xunyuan, Qin, Yan, Liu, Jinfeng, Huang, Biao
Other Authors: School of Chemistry, Chemical Engineering and Biotechnology
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173071
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1730712024-01-12T15:32:00Z Data-driven moving horizon state estimation of nonlinear processes using Koopman operator Yin, Xunyuan Qin, Yan Liu, Jinfeng Huang, Biao School of Chemistry, Chemical Engineering and Biotechnology Engineering::Chemical engineering Data-Driven State Estimation Nonlinear Process In this paper, a data-driven constrained state estimation method is proposed for nonlinear processes. Within the Koopman operator framework, we propose a data-driven model identification procedure for state estimation based on the algorithm of extended dynamic mode decomposition, which seeks an optimal approximation of the Koopman operator for a nonlinear process in a higher-dimensional space that correlates with the original process state-space via a prescribed nonlinear coordinate transformation. By implementing the proposed procedure, a linear state-space model can be established based on historic process data to describe the dynamics of a nonlinear process and the nonlinear dependence of the sensor measurements on process states. Based on the identified Koopman operator, a linear moving horizon estimation (MHE) algorithm that explicitly addresses constraints on the original process states is formulated to efficiently estimate the states in the higher-dimensional space. The states of the treated nonlinear process are recovered based on the state estimates provided by the MHE estimator designed in the higher-dimensional space. Two process examples are utilized to demonstrate the effectiveness and superiority of the proposed framework. Ministry of Education (MOE) Nanyang Technological University Submitted/Accepted version This research is supported by Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RS15/21), and Nanyang Technological University, Singapore (Start-Up Grant). 2024-01-10T07:18:15Z 2024-01-10T07:18:15Z 2023 Journal Article Yin, X., Qin, Y., Liu, J. & Huang, B. (2023). Data-driven moving horizon state estimation of nonlinear processes using Koopman operator. Chemical Engineering Research and Design, 200, 481-492. https://dx.doi.org/10.1016/j.cherd.2023.10.033 0263-8762 https://hdl.handle.net/10356/173071 10.1016/j.cherd.2023.10.033 2-s2.0-85177752092 200 481 492 en RS15/21 NTU-SUG Chemical Engineering Research and Design © 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1016/j.cherd.2023.10.033. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Chemical engineering
Data-Driven State Estimation
Nonlinear Process
spellingShingle Engineering::Chemical engineering
Data-Driven State Estimation
Nonlinear Process
Yin, Xunyuan
Qin, Yan
Liu, Jinfeng
Huang, Biao
Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
description In this paper, a data-driven constrained state estimation method is proposed for nonlinear processes. Within the Koopman operator framework, we propose a data-driven model identification procedure for state estimation based on the algorithm of extended dynamic mode decomposition, which seeks an optimal approximation of the Koopman operator for a nonlinear process in a higher-dimensional space that correlates with the original process state-space via a prescribed nonlinear coordinate transformation. By implementing the proposed procedure, a linear state-space model can be established based on historic process data to describe the dynamics of a nonlinear process and the nonlinear dependence of the sensor measurements on process states. Based on the identified Koopman operator, a linear moving horizon estimation (MHE) algorithm that explicitly addresses constraints on the original process states is formulated to efficiently estimate the states in the higher-dimensional space. The states of the treated nonlinear process are recovered based on the state estimates provided by the MHE estimator designed in the higher-dimensional space. Two process examples are utilized to demonstrate the effectiveness and superiority of the proposed framework.
author2 School of Chemistry, Chemical Engineering and Biotechnology
author_facet School of Chemistry, Chemical Engineering and Biotechnology
Yin, Xunyuan
Qin, Yan
Liu, Jinfeng
Huang, Biao
format Article
author Yin, Xunyuan
Qin, Yan
Liu, Jinfeng
Huang, Biao
author_sort Yin, Xunyuan
title Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
title_short Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
title_full Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
title_fullStr Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
title_full_unstemmed Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
title_sort data-driven moving horizon state estimation of nonlinear processes using koopman operator
publishDate 2024
url https://hdl.handle.net/10356/173071
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