57 citations · 213 across the 28 of their papers we have counts for
12 papers · 1 filter
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Sayak Mukherjee, Ramij R. Hossain, Sheik M. Mohiuddin +7
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient con…
Resilient Communication Scheme for Distributed Decision of InterconnectingNetworks of Microgrids
Thanh Long Vu, Sayak Mukherjee, Veronica Adetola
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 ther…
Neural Lyapunov Differentiable Predictive Control
Sayak Mukherjee, Ján Drgoňa, Aaron Tuor +2
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapuno…
Data-Driven Pole Placement in LMI Regions with Robustness Constraints
Sayak Mukherjee, Ramij R. Hossain
This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane. We considered the system state and input matrices t…
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
Thanh Long Vu, Sayak Mukherjee, Renke Huang +1
Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe condition…
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks
Sayak Mukherjee, Veronica Adetola
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 at…
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
Sayak Mukherjee, Renke Huang, Qiuhua Huang +2
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive select…
Safe Reinforcement Learning for Emergency LoadShedding of Power Systems
Thanh Long Vu, Sayak Mukherjee, Tim Yin +3
The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertai…
Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix
Sayak Mukherjee, Thanh Long Vu
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…
Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator
Sayak Mukherjee, Thanh Long Vu
This paper discusses learning a structured feedback control to obtain sufficient robustness to exogenous inputs for linear dynamic systems with unknown state matrix. The structural…
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations
Sayak Mukherjee, He Bai, Aranya Chakrabortty
We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems. We first…
Modeling and Quantifying the Impact of Wind Power Penetration on Power System Coherency
Sayak Mukherjee, Aranya Chakrabortty, Saman Babaei
This paper presents a mathematical analysis of how wind generation impacts the coherency property of power systems. Coherency arises from time-scale separation in the dynamics of s…