Many industrial processes utilize proportional-integral-derivative (PID) controllers due to
their practicality and often satisfactory performance. The proper controller parameters …

Traditional assembly line requires a significant amount of designs from engineers,
especially in the case of multi-species and small-lot production. Recently, intelligent …

Reinforcement learning (RL) for continuous state/action space systems has remained a
challenge for nonlinear multivariate dynamical systems even at a simulation level …

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes
pose a challenge for process control. Due to the absence of accurate models and resulting …

This paper synchronizes control theory with computer vision by formalizing object tracking
as a sequential decision-making process. A reinforcement learning (RL) agent successfully …

In this paper, we propose a deep reinforcement learning (DRL) based predictive control
scheme for reducing the energy consumption and energy cost of pumping systems in …

Alarm floods are commonly present in modern complex industrial process, and usually
degrade the performance of the alarm systems severely. In an alarm flood, the massive …

Modern process controllers necessitate high quality models and remedial system re-
identification upon performance degradation. Reinforcement Learning (RL) can be a …

Capacitive deionization (CDI) is an alternative desalination technology that uses
electrochemical ion separation. Although several attempts have been made to maximize the …

Vibration and noise during the spin-dry process in a washing machine are the “pain points”
of greatest concern. These are mainly caused by an unbalanced drum that occurs from the …