Machine learning in process systems engineering: Challenges and opportunities
This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
Reinforcement learning for batch process control: Review and perspectives
H Yoo, HE Byun, D Han, JH Lee - Annual Reviews in Control, 2021 - Elsevier
Batch or semi-batch processing is becoming more prevalent in industrial chemical
manufacturing but it has not benefited from advanced control technologies to a same degree …
manufacturing but it has not benefited from advanced control technologies to a same degree …
Safe and fast tracking on a robot manipulator: Robust mpc and neural network control
Fast feedback control and safety guarantees are essential in modern robotics. We present
an approach that achieves both by combining novel robust model predictive control (MPC) …
an approach that achieves both by combining novel robust model predictive control (MPC) …
Deepopf: A deep neural network approach for security-constrained dc optimal power flow
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …
[HTML][HTML] do-mpc: Towards FAIR nonlinear and robust model predictive control
Over the last decades, model predictive control (MPC) has shown outstanding performance
for control tasks from various domains. This performance has further improved in recent …
for control tasks from various domains. This performance has further improved in recent …
Deep learning-based long-horizon MPC: robust, high performing, and computationally efficient control for PMSM drives
M Abu-Ali, F Berkel, M Manderla… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
This article presents a computationally efficient and high performing approximate long-
horizon model predictive control (MPC) for permanent magnet synchronous motors …
horizon model predictive control (MPC) for permanent magnet synchronous motors …
Fusion of machine learning and MPC under uncertainty: What advances are on the horizon?
This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …
learning and model predictive control under uncertainty. The paper is organized as a …
Integrated decision and control: Toward interpretable and computationally efficient driving intelligence
Decision and control are core functionalities of high-level automated vehicles. Current
mainstream methods, such as functional decomposition and end-to-end reinforcement …
mainstream methods, such as functional decomposition and end-to-end reinforcement …
Deepopf: deep neural networks for optimal power flow
X Pan - Proceedings of the 8th ACM International Conference …, 2021 - dl.acm.org
We develop a Deep Neural Network (DNN) approach, namely DeepOPF, for solving optimal
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …
Lyapunov-based real-time and iterative adjustment of deep neural networks
A real-time Deep Neural Network (DNN) adaptive control architecture is developed for
general uncertain nonlinear dynamical systems to track a desired time-varying trajectory. A …
general uncertain nonlinear dynamical systems to track a desired time-varying trajectory. A …