| 1 |
Stochastic Control Framework for Determining Feasible Alternatives in Sampling Allocation
Peng YJ, Song J, Xu J, Chong EKP
IEEE Transactions on Automatic Control, 65(6), 2647, 2020 |
| 2 |
A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system
Kim JW, Park BJ, Yoo H, Oh TH, Lee JH, Lee JM
Journal of Process Control, 87, 166, 2020 |
| 3 |
Demand side energy management of EV charging stations by approximate dynamic programming
Wu Y, Ravey A, Chrenko D, Miraoui A
Energy Conversion and Management, 196, 878, 2019 |
| 4 |
Symbolic Optimal Control
Reissig G, Rungger M
IEEE Transactions on Automatic Control, 64(6), 2224, 2019 |
| 5 |
Online H infinity control for completely unknown nonlinear systems via an identifier-critic-based ADP structure
Lv YF, Na J, Ren XM
International Journal of Control, 92(1), 100, 2019 |
| 6 |
Hedging Strategies: Electricity Investment Decisions under Policy Uncertainty
Morris JF, Srikrishnan V, Webster MD, Reilly JM
Energy Journal, 39(1), 101, 2018 |
| 7 |
An approximate dynamic programming method for the optimal control of Alkai-Surfactant-Polymer flooding
Ge YL, Li SR, Chan P
Journal of Process Control, 64, 15, 2018 |
| 8 |
Model predictive control under forecast uncertainty for optimal operation of buildings with integrated solar systems
Liu X, Paritosh P, Awalgaonkar NM, Bilionis I, Karava P
Solar Energy, 171, 953, 2018 |
| 9 |
Data-driven approximate value iteration with optimality error bound analysis
Li YQ, Hou ZS, Feng YJ, Chi RH
Automatica, 78, 79, 2017 |
| 10 |
Drift counteraction optimal control for deterministic systems and enhancing convergence of value iteration
Zidek RAE, Kolmanovsky IV
Automatica, 83, 108, 2017 |
