Reduced-order Koopman modeling and predictive control of nonlinear processes

• Main Authors: Zhang, Xuewen, Han, Minghao, Yin, Xunyuan
• Other Authors: School of Chemistry, Chemical Engineering and Biotechnology
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering::Chemical engineering; Data-Driven Control; Koopman Operator
• Online Access: https://hdl.handle.net/10356/173081
• Institution: Nanyang Technological University

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 lifting functions are used to project the original nonlinear state–space into a higher-dimensional linear function space, in which Koopman-based linear models can be constructed for the underlying nonlinear process. To curb the significant increase in the dimensionality of the resulting full-order Koopman models caused by the use of lifting functions, we propose a reduced-order Koopman modeling approach based on proper orthogonal decomposition. A computationally efficient linear robust predictive control scheme is established based on the reduced-order Koopman model. A case study on a benchmark chemical process is conducted to illustrate the effectiveness of the proposed method. Comprehensive comparisons are conducted to demonstrate the advantage of the proposed method.