Dec 16, 2023 · In this article, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process ...
Apr 10, 2024 · Abstract:In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process ...
In this article, we consider a state estimation problem for large‐scale nonlinear processes in the absence of first‐principles process models.
Apr 10, 2024 · Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes. Xiaojie ...
We propose a Koopman-based data-driven modeling method for general nonlinear processes for the state estimation purpose, which can provide infinite- ...
Arbabi, H., Korda, M., Mezić, I., 2018. A data-driven Koopman model predictive control framework for nonlinear partial differential equations. In: IEEE ...
Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes ... A parallel subsystem ...
This paper presents three novel moving-horizon estimation (MHE) methods for discrete-time partitioned linear systems, i.e., systems decomposed into coupled ...
In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator.
May 24, 2024 · Data-driven parallel Koopman subsystem · modeling and distributed moving horizon state · estimation for large-scale nonlinear processes.