Tae Hoon Oh

发表

Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences

Sang Hwan Son, Byung Jun Park, Tae Hoon Oh, 2020, Int. J. Control.

Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor

Byung Jun Park, Tae Hoon Oh, Jong Woo Kim, 2021, Comput. Chem. Eng..

Learning of model-plant mismatch map via neural network modeling and its application to offset-free model predictive control

Dong Hwi Jeong, Tae Hoon Oh, Jong Woo Kim, 2022, Journal of Process Control.

Multi-strategy control to extend the feasibility region for robust model predictive control

Dong Hwi Jeong, Tae Hoon Oh, Jong Woo Kim, 2022, Journal of Process Control.

Hybrid Model Predictive Control for Hybrid Electric Vehicle Energy Management Using an Efficient Mixed-Integer Formulation

Tae Hoon Oh, Jong Min Lee, Heeyun Lee, 2022, IFAC-PapersOnLine.

Primal-dual differential dynamic programming: A model-based reinforcement learning for constrained dynamic optimization

Tae Hoon Oh, Jong Woo Kim, Jong Min Lee, 2022, Comput. Chem. Eng..

A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

Byung Jun Park, Jong Min Lee, Jay H. Lee, 2020 .

Conceptual Design of an Energy-Efficient Process for Separating Aromatic Compounds from Naphtha with a High Concentration of Aromatic Compounds Using 4-Methyl-N-butylpyridinium Tetrafluoroborate Ionic Liquid

Tae Hoon Oh, J. M. Lee, Se-Kyu Oh, 2017 .

Model-plant mismatch learning offset-free model predictive control

Tae Hoon Oh, Jong Woo Kim, J. M. Lee, 2020, ArXiv.

Move blocked model predictive control with improved optimality using semi-explicit approach for applying time-varying blocking structure

Sang Hwan Son, Jong Min Lee, Jong Woo Kim, 2020 .

Multiple Tube Model Predictive Control with Decomposed Disturbance Subsets Approach for Less Conservative Control of Worst-Case Cost

Jong Min Lee, Tae Hoon Oh, Tae Hoon Oh, 2018, 2018 18th International Conference on Control, Automation and Systems (ICCAS).

Integration of Reinforcement Learning and Model Predictive Control to Optimize Semi‐batch Bioreactor

Tae Hoon Oh, Jong Woo Kim, Jong Min Lee, 2022, AIChE Journal.

Transition Model for Simulated Moving Bed Under Nonideal Conditions

Tae Hoon Oh, J. M. Lee, Se-Kyu Oh, 2019, Industrial & Engineering Chemistry Research.

Integrating Path Integral Control With Backstepping Control to Regulate Stochastic System

Tae Hoon Oh, Jong Woo Kim, Jong Min Lee, 2023, International Journal of Control, Automation and Systems.

Automatic control of simulated moving bed process with deep Q-network.

Tae Hoon Oh, Jong Woo Kim, J. M. Lee, 2021, Journal of chromatography. A.

Backstepping control integrated with Lyapunov-based model predictive control

Jong Min Lee, Tae Hoon Oh, Yeon-Soo Kim, 2019, Journal of Process Control.

Convergence analysis of the deep neural networks based globalized dual heuristic programming

Sang Hwan Son, Dong Hwi Jeong, Tae Hoon Oh, 2020, Autom..

Quantitative comparison of reinforcement learning and data-driven model predictive control for chemical and biological processes

Tae Hoon Oh, 2023, Computers & Chemical Engineering.