Reduced-order Koopman modeling and predictive control of nonlinear processes
In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting functions for Koopman identification. The selected...
Saved in:
Main Authors: | Zhang, Xuewen, Han, Minghao, Yin, Xunyuan |
---|---|
Other Authors: | School of Chemistry, Chemical Engineering and Biotechnology |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173081 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Robust learning and control of time-delay nonlinear systems with deep recurrent Koopman operators
by: Han, Minghao, et al.
Published: (2024) -
Efficient economic model predictive control of water treatment process with the learning-based Koopman operator
by: Han, Minghao, et al.
Published: (2024) -
Data-driven moving horizon state estimation of nonlinear processes using Koopman operator
by: Yin, Xunyuan, et al.
Published: (2024) -
KOOPMAN-BASED METHODS AND OPTIMALLY TIME-DEPENDENT MODES FOR THE DATA-DRIVEN ANALYSIS OF EXTREME EVENTS
by: MATHIEU PEEL
Published: (2024) -
Deep Koopman operator-informed safety command governor for autonomous vehicles
by: Chen, Hao, et al.
Published: (2024)