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Sayak Mukherjee
Sayak Mukherjee
pnnl.gov의 이메일 확인됨 - 홈페이지
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Reduced-dimensional reinforcement learning control using singular perturbation approximations
S Mukherjee, H Bai, A Chakrabortty
Automatica 126, 109451, 2021
422021
On model-free reinforcement learning of reduced-order optimal control for singularly perturbed systems
S Mukherjee, H Bai, A Chakrabortty
2018 IEEE Conference on Decision and Control (CDC), 5288-5293, 2018
412018
Scalable designs for reinforcement learning-based wide-area damping control
S Mukherjee, A Chakrabortty, H Bai, A Darvishi, B Fardanesh
IEEE Transactions on Smart Grid 12 (3), 2389-2401, 2021
292021
Safe reinforcement learning for emergency load shedding of power systems
TL Vu, S Mukherjee, T Yin, R Huang, J Tan, Q Huang
2021 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2021
192021
Barrier function-based safe reinforcement learning for emergency control of power systems
TL Vu, S Mukherjee, R Huang, Q Huang
2021 60th IEEE Conference on Decision and Control (CDC), 3652-3657, 2021
152021
Learning Stochastic Parametric Diferentiable Predictive Control Policies
J Drgoňa, S Mukherjee, A Tuor, M Halappanavar, D Vrabie
IFAC-PapersOnLine 55 (25), 121-126, 2022
132022
Neural lyapunov differentiable predictive control
S Mukherjee, J Drgoňa, A Tuor, M Halappanavar, D Vrabie
2022 IEEE 61st Conference on Decision and Control (CDC), 2097-2104, 2022
112022
Modeling and quantifying the impact of wind penetration on slow coherency of power systems
S Mukherjee, A Chakrabortty, S Babaei
IEEE Transactions on Power Systems 36 (2), 1002-1012, 2020
112020
Block-decentralized model-free reinforcement learning control of two time-scale networks
S Mukherjee, A Chakrabortty, H Bai
2019 American Control Conference (ACC), 2233-2238, 2019
112019
Reinforcement learning of structured stabilizing control for linear systems with unknown state matrix
S Mukherjee, TL Vu
IEEE Transactions on Automatic Control 68 (3), 1746-1752, 2022
102022
Scalable voltage control using structure-driven hierarchical deep reinforcement learning
S Mukherjee, R Huang, Q Huang, TL Vu, T Yin
arXiv preprint arXiv:2102.00077, 2021
92021
Measurement-driven optimal control of utility-scale power systems: A New York State grid perspective
S Mukherjee, S Babaei, A Chakrabortty, B Fardanesh
International Journal of Electrical Power & Energy Systems 115, 105470, 2020
92020
A measurement-based approach for optimal damping control of the New York state power grid
S Mukherjee, S Babaei, A Chakrabortty
2018 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2018
82018
Model-based and model-free designs for an extended continuous-time LQR with exogenous inputs
S Mukherjee, H Bai, A Chakrabortty
Systems & Control Letters 154, 104983, 2021
72021
On robust model-free reduced-dimensional reinforcement learning control for singularly perturbed systems
S Mukherjee, H Bai, A Chakrabortty
2020 American Control Conference (ACC), 3914-3919, 2020
72020
Economic generation scheduling in microgrid with pumped-hydro unit using particle swarm optimization
S Mukherjee, R Chakraborty, SK Goswami
2015 IEEE International Conference on Electrical, Computer and Communication …, 2015
72015
Adversar: Adversarial search and rescue via multi-agent reinforcement learning
A Rahman, A Bhattacharya, T Ramachandran, S Mukherjee, H Sharma, ...
2022 IEEE International Symposium on Technologies for Homeland Security (HST …, 2022
52022
Model-free decentralized reinforcement learning control of distributed energy resources
S Mukherjee, H Bai, A Chakrabortty
2020 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2020
52020
Learning power system dynamic signatures using LSTM-based deep neural network: A prototype study on the New York state grid
S Mukherjee, A Darvishi, A Chakrabortty, B Fardanesh
2019 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2019
52019
On the stochastic stability of deep markov models
J Drgona, S Mukherjee, J Zhang, F Liu, M Halappanavar
Advances in Neural Information Processing Systems 34, 24033-24047, 2021
42021
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