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

• 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: Engineering::Chemical engineering; Data-Driven State Estimation; Nonlinear Process
• Online Access: https://hdl.handle.net/10356/173071
• Institution: Nanyang Technological University

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...