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 |
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Other Authors: | School of Chemistry, Chemical Engineering and Biotechnology |
Format: | Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/173071 |
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Institution: | Nanyang Technological University |
Language: | English |
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