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Found 17 papers, 2 papers with code
MAPL: Model Agnostic Peer-to-peer Learning
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature.
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs).
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning
Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration.
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
Resilient Communication Scheme for Distributed Decision of InterconnectingNetworks of Microgrids
Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system.
Neural Lyapunov Differentiable Predictive Control
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees.
Learning Stochastic Parametric Differentiable Predictive Control Policies
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods.
Safe Reinforcement Learning for Grid Voltage Control
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently.
Data-Driven Pole Placement in LMI Regions with Robustness Constraints
This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane.
On the Stochastic Stability of Deep Markov Models
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers.
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks
This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack.
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
We exploit the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models.
Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator
This paper discusses learning a structured feedback control to obtain sufficient robustness to exogenous inputs for linear dynamic systems with unknown state matrix.
Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix
This paper delves into designing stabilizing feedback control gains for continuous linear systems with unknown state matrix, in which the control is subject to a general structural constraint.
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations
We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems.
Connecting the dots across time: Reconstruction of single cell signaling trajectories using time-stamped data
Is it possible to use the time stamped cytometry data to reconstruct single cell signaling trajectories?
