Reduced-order Koopman modeling and predictive control of nonlinear processes
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要