4 papers
Learning-based data-enabled moving horizon estimation with application to membrane-based biological wastewater treatment process
Xiaojie Li, Xunyuan Yin
In this paper, we propose a data-enabled moving horizon estimation (MHE) approach for a class of nonlinear systems without explicit modeling, by leveraging Koopman operator theory…
Approximating arrival costs in distributed moving horizon estimation: A recursive method
Xiaojie Li, Xunyuan Yin
In this paper, we present a new approach to distributed moving horizon estimation for constrained nonlinear processes. The method involves approximating the arrival costs of local…
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
Xiaojie Li, Song Bo, Xuewen Zhang +2
In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process models. By exploiting process operation data, bo…
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
Xiaojie Li, Adrian Wing-Keung Law, Xunyuan Yin
In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formula…