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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Bibliographic Details
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
Subjects:
Online Access:https://hdl.handle.net/10356/173071
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Institution: Nanyang Technological University
Language: English