We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Data-driven moving horizon state estimation of nonlinear processes using Koopman operator.
- Authors
Yin, Xunyuan; Qin, Yan; Liu, Jinfeng; Huang, Biao
- Abstract
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 optimal approximation of the Koopman operator for a nonlinear process in a higher-dimensional space that correlates with the original process state-space via a prescribed nonlinear coordinate transformation. By implementing the proposed procedure, a linear state-space model can be established based on historic process data to describe the dynamics of a nonlinear process and the nonlinear dependence of the sensor measurements on process states. Based on the identified Koopman operator, a linear moving horizon estimation (MHE) algorithm that explicitly addresses constraints on the original process states is formulated to efficiently estimate the states in the higher-dimensional space. The states of the treated nonlinear process are recovered based on the state estimates provided by the MHE estimator designed in the higher-dimensional space. Two process examples are utilized to demonstrate the effectiveness and superiority of the proposed framework. • We propose a Koopman-based data-driven modeling method for general nonlinear processes for the state estimation purpose, which can provide infinite- step-ahead state predictions. • We develop a linear moving horizon estimation scheme to handle constrained nonlinear state estimation problems for general nonlinear processes. • We present two case studies, including an experimental study on a water-tank process consisting of four interconnected water tanks.
- Subjects
NONLINEAR estimation; COORDINATE transformations; NONLINEAR operators; KALMAN filtering; NONLINEAR equations
- Publication
Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, Vol 200, p481
- ISSN
0263-8762
- Publication type
Article
- DOI
10.1016/j.cherd.2023.10.033