11 citations · 11 across the 13 of their papers we have counts for
13 papers · 1 filter
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…
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…
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling
Mingxue Yan, Minghao Han, Adrian Wing-Keung Law +1
In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlin…
Approximating arrival costs in distributed moving horizon estimation: A recursive method
Xiaojie Li, Xunyuan Yin
In this paper, we present a new approach to distributed moving horizon estimation for constrained nonlinear processes. The method involves approximating the arrival costs of local…
Efficient Economic Model Predictive Control of Water Treatment Process with Learning-based Koopman Operator
Minghao Han, Jingshi Yao, Adrian Wing-Keung Law +1
Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational perfor…
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…
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
Xiaojie Li, Adrian Wing-Keung Law, Xunyuan Yin
In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formula…
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…