7 papers
Learning-based data-enabled moving horizon estimation with application to membrane-based biological wastewater treatment process
Xiaojie Li, Xunyuan Yin
In this paper, we propose a data-enabled moving horizon estimation (MHE) approach for a class of nonlinear systems without explicit modeling, by leveraging Koopman operator theory…
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…
MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems
Minghao Han, Kiwan Wong, Adrian Wing-Keung Law +1
In this work, we propose a meta-learning-based Koopman modeling and predictive control approach for nonlinear systems with parametric uncertainties. An adaptive deep meta-learning-…
Optimal Control of Markov Decision Processes for Efficiency with Linear Temporal Logic Tasks
Yu Chen, Xuanyuan Yin, Shaoyuan Li +1
We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the…
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 Neural Koopman Operator-based Economic Model Predictive Control of Shipboard Carbon Capture System
Minghao Han, Xunyuan Yin
Shipboard carbon capture is a promising solution to help reduce carbon emissions in international shipping. In this work, we propose a data-driven dynamic modeling and economic pre…