Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
A review on reinforcement learning: Introduction and applications in industrial process control
In recent years, reinforcement learning (RL) has attracted significant attention from both
industry and academia due to its success in solving some complex problems. This paper …
industry and academia due to its success in solving some complex problems. This paper …
Efficient and accurate estimation of lipschitz constants for deep neural networks
Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many
applications ranging from robustness certification of classifiers to stability analysis of closed …
applications ranging from robustness certification of classifiers to stability analysis of closed …
Stability analysis using quadratic constraints for systems with neural network controllers
A method is presented to analyze the stability of feedback systems with neural network
controllers. Two stability theorems are given to prove asymptotic stability and to compute an …
controllers. Two stability theorems are given to prove asymptotic stability and to compute an …
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
N Sadeghianpourhamami, J Deleu… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Demand response (DR) becomes critical to manage the charging load of a growing electric
vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but …
vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but …
Enforcing robust control guarantees within neural network policies
When designing controllers for safety-critical systems, practitioners often face a challenging
tradeoff between robustness and performance. While robust control methods provide …
tradeoff between robustness and performance. While robust control methods provide …
(Deep) reinforcement learning for electric power system control and related problems: A short review and perspectives
M Glavic - Annual Reviews in Control, 2019 - Elsevier
This paper reviews existing works on (deep) reinforcement learning considerations in
electric power system control. The works are reviewed as they relate to electric power …
electric power system control. The works are reviewed as they relate to electric power …
Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
Imitation learning with stability and safety guarantees
A method is presented to learn neural network (NN) controllers with stability and safety
guarantees through imitation learning (IL). Convex stability and safety conditions are derived …
guarantees through imitation learning (IL). Convex stability and safety conditions are derived …
Wide-area measurement system-based low frequency oscillation damping control through reinforcement learning
Ensuring the stability of power systems is gaining more attention today than ever before due
to the rapid growth of uncertainties in load and increased renewable energy penetration …
to the rapid growth of uncertainties in load and increased renewable energy penetration …