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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/173071 |
| _version_ | 1826113809062821888 |
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| author | Yin, Xunyuan Qin, Yan Liu, Jinfeng Huang, Biao |
| 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 |
| author_sort | Yin, Xunyuan |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T03:29:19Z |
| format | Journal Article |
| id | ntu-10356/173071 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:29:19Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Chemical engineering Data-Driven State Estimation Nonlinear Process |
| url | https://hdl.handle.net/10356/173071 |
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