Reduced-order Koopman modeling and predictive control of nonlinear processes
<|reference_start|>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 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.<|reference_end|>
@article{zhang2024reduced-order,
    title={Reduced-order Koopman modeling and predictive control of nonlinear
  processes},
    author={Xuewen Zhang, Minghao Han, Xunyuan Yin},
    journal={Computers & Chemical Engineering, 2023, 179, p.108440},
    year={2024},
    doi={10.1016/j.compchemeng.2023.108440},
    archivePrefix={arXiv},
    eprint={2404.00553},
    primaryClass={eess.SY cs.SY}
}