32 citations · 32 across the 6 of their papers we have counts for
9 papers · 1 filter
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-…
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…
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…
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…
Lyapunov-based reinforcement learning for distributed control with stability guarantee
Jingshi Yao, Minghao Han, Xunyuan Yin
In this paper, we propose a Lyapunov-based reinforcement learning method for distributed control of nonlinear systems comprising interacting subsystems with guaranteed closed-loop…
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
Zhaoyang Li, Minghao Han, Dat-Nguyen Vo +1
Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly de…