  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.
