32 citations · 35 across the 7 of their papers we have counts for
7 papers
Learning-based data-enabled economic predictive control with convex optimization for nonlinear systems
Mingxue Yan, Xuewen Zhang, Kaixiang Zhang +2
In this article, we propose a data-enabled economic predictive control method for a class of nonlinear systems, which aims to optimize the economic operational performance while ha…
Economic zone data-enabled predictive control for connected open water systems
Xiaoqiao Chen, Xuewen Zhang, Minghao Han +2
The real-time operation of open water systems is essential for ensuring operational safety, satisfying operational requirements, and optimizing energy usage. However, existing rule…
Economic data-enabled predictive control using machine learning
Mingxue Yan, Xuewen Zhang, Kaixiang Zhang +2
In this paper, we propose a convex data-based economic predictive control method within the framework of data-enabled predictive control (DeePC). Specifically, we use a neural netw…
Machine learning-based hybrid dynamic modeling and economic predictive control of carbon capture process for ship decarbonization
Xuewen Zhang, Kuniadi Wandy Huang, Dat-Nguyen Vo +3
Implementing carbon capture technology on-board ships holds promise as a solution to facilitate the reduction of carbon intensity in international shipping, as mandated by the Inte…
Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning
Xuewen Zhang, Kaixiang Zhang, Zhaojian Li +1
Data-enabled predictive control (DeePC) is a data-driven control algorithm that utilizes data matrices to form a non-parametric representation of the underlying system, predicting…
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
Xiaojie Li, Song Bo, Xuewen Zhang +2
In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process models. By exploiting process operation data, bo…
Reduced-order Koopman modeling and predictive control of nonlinear processes
Xuewen Zhang, Minghao Han, Xunyuan Yin
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 iden…