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 …
Industrial data science–a review of machine learning applications for chemical and process industries
M Mowbray, M Vallerio, C Perez-Galvan… - Reaction Chemistry & …, 2022 - pubs.rsc.org
In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …
start with examples that are irrelevant to process engineers (eg classification of images …
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …
Dynamics-aware unsupervised discovery of skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model
for the dynamics of the environment. A good model can potentially enable planning …
for the dynamics of the environment. A good model can potentially enable planning …
Learning-based model predictive control for safe exploration
T Koller, F Berkenkamp, M Turchetta… - 2018 IEEE conference …, 2018 - ieeexplore.ieee.org
Learning-based methods have been successful in solving complex control tasks without
significant prior knowledge about the system. However, these methods typically do not …
significant prior knowledge about the system. However, these methods typically do not …
Cautious model predictive control using gaussian process regression
Gaussian process (GP) regression has been widely used in supervised machine learning
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …
[PDF][PDF] DeepMPC: Learning deep latent features for model predictive control.
Designing controllers for tasks with complex nonlinear dynamics is extremely challenging,
time-consuming, and in many cases, infeasible. This difficulty is exacerbated in tasks such …
time-consuming, and in many cases, infeasible. This difficulty is exacerbated in tasks such …
Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
Today's manufacturing processes are pushed to their limits to generate products with ever-
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …
MLCAD: A survey of research in machine learning for CAD keynote paper
Due to the increasing size of integrated circuits (ICs), their design and optimization phases
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
Model learning for robot control: a survey
D Nguyen-Tuong, J Peters - Cognitive processing, 2011 - Springer
Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …
dynamics models of the robot's own body and controllable external objects. It is widely …