Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
[HTML][HTML] All you need to know about model predictive control for buildings
It has been proven that advanced building control, like model predictive control (MPC), can
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …
The safety filter: A unified view of safety-critical control in autonomous systems
Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …
by the expanding reach of robotic technologies. However, the emergence of new …
A review of second-life lithium-ion batteries for stationary energy storage applications
The large-scale retirement of electric vehicle traction batteries poses a huge challenge to
environmental protection and resource recovery since the batteries are usually replaced …
environmental protection and resource recovery since the batteries are usually replaced …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
Stochastic model predictive control—how does it work?
Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of
systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance …
systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance …
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 …
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …
including the reliable integration of renewable energy sources into power grids, safe …
Optimal exploration for model-based rl in nonlinear systems
A Wagenmaker, G Shi… - Advances in Neural …, 2024 - proceedings.neurips.cc
Learning to control unknown nonlinear dynamical systems is a fundamental problem in
reinforcement learning and control theory. A commonly applied approach is to first explore …
reinforcement learning and control theory. A commonly applied approach is to first explore …
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 …