1 citations · 3 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
A semi-centralized multi-agent RL framework for efficient irrigation scheduling
Bernard T. Agyeman, Benjamin Decard-Nelson, Jinfeng Liu +1
This paper proposes a Semi-Centralized Multi-Agent Reinforcement Learning (SCMARL) approach for irrigation scheduling in spatially variable agricultural fields, where management zo…
Computing control invariant sets of nonlinear systems: decomposition and distributed computing
Benjamin Decardi-Nelson, Jinfeng Liu
In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the…
An efficient implementation of graph-based invariant set algorithm for constrained nonlinear dynamical systems
Benjamin Decardi-Nelsona, Jinfeng Liu
The graph-based invariant set (GIS) algorithm is a promising set-based technique for computing the largest (with respect to inclusion) control invariant set of general discrete-tim…
Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise
Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson +3
Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, which one shoul…
Robust economic MPC of the absorption column in post-combustion carbon capture through zone tracking
Benjamin Decardi-Nelson, Jinfeng Liu
Several studies have reported the importance of optimally operating the absorption column in a post-combustion CO2 capture (PCC) plant. It has been demonstrated in our previous wor…