| 1 |
A Universal Empirical Dynamic Programming Algorithm for Continuous State MDPs
Haskell WB, Jain R, Sharma H, Yu PQ
IEEE Transactions on Automatic Control, 65(1), 115, 2020 |
| 2 |
Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning
Hosseini E, Aghadavoodi E, Ramirez LMF
Renewable Energy, 157, 897, 2020 |
| 3 |
Stability of Stochastic Approximations With "Controlled Markov" Noise and Temporal Difference Learning
Ramaswamy A, Bhatnagar S
IEEE Transactions on Automatic Control, 64(6), 2614, 2019 |
| 4 |
Reinforcement Learning-Based Adaptive Optimal Exponential Tracking Control of Linear Systems With Unknown Dynamics
Chen C, Modares H, Xie K, Lewis FL, Wan Y, Xie SL
IEEE Transactions on Automatic Control, 64(11), 4423, 2019 |
| 5 |
Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids
Kofinas P, Dounis AI, Vouros GA
Applied Energy, 219, 53, 2018 |
| 6 |
Optimal Synchronization of Heterogeneous Nonlinear Systems With Unknown Dynamics
Modares H, Lewis FL, Kang W, Davoudi A
IEEE Transactions on Automatic Control, 63(1), 117, 2018 |
| 7 |
Port-Hamiltonian Systems in Adaptive and Learning Control: A Survey
Nageshrao SP, Lopes GAD, Jeltsema D, Babuska R
IEEE Transactions on Automatic Control, 61(5), 1223, 2016 |
| 8 |
Semi-Markov decision problems and performance sensitivity analysis
Cao XR
IEEE Transactions on Automatic Control, 48(5), 758, 2003 |
