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Jay Hyung Lee
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Model predictive control: past, present and future
M Morari, JH Lee
Computers & chemical engineering 23 (4-5), 667-682, 1999
32161999
Model predictive control: Review of the three decades of development
JH Lee
International Journal of Control, Automation and Systems 9, 415-424, 2011
7842011
Constrained linear state estimation—a moving horizon approach
CV Rao, JB Rawlings, JH Lee
Automatica 37 (10), 1619-1628, 2001
7232001
Cellulose crystallinity–a key predictor of the enzymatic hydrolysis rate
M Hall, P Bansal, JH Lee, MJ Realff, AS Bommarius
The FEBS journal 277 (6), 1571-1582, 2010
6882010
Model-based iterative learning control with a quadratic criterion for time-varying linear systems
JH Lee, KS Lee, WC Kim
Automatica 36 (5), 641-657, 2000
5682000
Worst-case formulations of model predictive control for systems with bounded parameters
JH Lee, Z Yu
Automatica 33 (5), 763-781, 1997
5361997
Modeling cellulase kinetics on lignocellulosic substrates
P Bansal, M Hall, MJ Realff, JH Lee, AS Bommarius
Biotechnology advances 27 (6), 833-848, 2009
5252009
State-space interpretation of model predictive control
JH Lee, M Morari, CE Garcia
Automatica 30 (4), 707-717, 1994
5021994
Extended Kalman filter based nonlinear model predictive control
JH Lee, NL Ricker
Industrial & Engineering Chemistry Research 33 (6), 1530-1541, 1994
4681994
Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty
J Kim, MJ Realff, JH Lee
Computers & Chemical Engineering 35 (9), 1738-1751, 2011
4662011
A moving horizon‐based approach for least‐squares estimation
DG Robertson, JH Lee, JB Rawlings
AIChE Journal 42 (8), 2209-2224, 1996
4621996
Machine Learning: Overview of the Recent Progresses and Implications for the Process Systems Engineering Field
JH Lee, J Shin, MJ Realff
Computers & Chemical Engineering 114, 111-121, 2018
4562018
Iterative learning control applied to batch processes: An overview
JH Lee, KS Lee
Control Engineering Practice 15 (10), 1306-1318, 2007
3352007
Reinforcement Learning–Overview of recent progress and implications for process control
J Shin, TA Badgwell, KH Liu, JH Lee
Computers & Chemical Engineering 127, 282-294, 2019
325*2019
Design of biomass processing network for biofuel production using an MILP model
J Kim, MJ Realff, JH Lee, C Whittaker, L Furtner
Biomass and bioenergy 35 (2), 853-871, 2011
3032011
Model predictive control technique combined with iterative learning for batch processes
KS Lee, IS Chin, HJ Lee, JH Lee
AIChE Journal 45 (10), 2175-2187, 1999
2871999
Nonlinear model predictive control of the Tennessee Eastman challenge process
NL Ricker, JH Lee
Computers & Chemical Engineering 19 (9), 961-981, 1995
2761995
Receding horizon recursive state estimation
KR Muske, JB Rawlings, JH Lee
1993 American Control Conference, 900-904, 1993
2411993
Fault detection and classification using artificial neural networks
S Heo, JH Lee
IFAC-PapersOnLine 51 (18), 470-475, 2018
2302018
Limitations of dynamic matrix control
P Lundström, JH Lee, M Morari, S Skogestad
Computers & Chemical Engineering 19 (4), 409-421, 1995
2281995
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