| Model predictive control: past, present and future M Morari, JH Lee Computers & chemical engineering 23 (4-5), 667-682, 1999 | 3392 | 1999 |
| Model predictive control: Review of the three decades of development JH Lee International Journal of Control, Automation and Systems 9, 415-424, 2011 | 853 | 2011 |
| Constrained linear state estimation—a moving horizon approach CV Rao, JB Rawlings, JH Lee Automatica 37 (10), 1619-1628, 2001 | 777 | 2001 |
| 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 | 736 | 2010 |
| 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 | 585 | 2000 |
| Worst-case formulations of model predictive control for systems with bounded parameters JH Lee, Z Yu Automatica 33 (5), 763-781, 1997 | 546 | 1997 |
| Modeling cellulase kinetics on lignocellulosic substrates P Bansal, M Hall, MJ Realff, JH Lee, AS Bommarius Biotechnology advances 27 (6), 833-848, 2009 | 541 | 2009 |
| 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 | 513 | 2018 |
| State-space interpretation of model predictive control JH Lee, M Morari, CE Garcia Automatica 30 (4), 707-717, 1994 | 507 | 1994 |
| Extended Kalman filter based nonlinear model predictive control JH Lee, NL Ricker Industrial & Engineering Chemistry Research 33 (6), 1530-1541, 1994 | 492 | 1994 |
| A moving horizon‐based approach for least‐squares estimation DG Robertson, JH Lee, JB Rawlings AIChE Journal 42 (8), 2209-2224, 1996 | 481 | 1996 |
| 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 | 479 | 2011 |
| 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 | 400* | 2019 |
| Iterative learning control applied to batch processes: An overview JH Lee, KS Lee Control Engineering Practice 15 (10), 1306-1318, 2007 | 352 | 2007 |
| 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 | 307 | 2011 |
| 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 | 296 | 1999 |
| Process systems engineering–the generation next? EN Pistikopoulos, A Barbosa-Povoa, JH Lee, R Misener, A Mitsos, ... Computers & Chemical Engineering 147, 107252, 2021 | 295 | 2021 |
| Nonlinear model predictive control of the Tennessee Eastman challenge process NL Ricker, JH Lee Computers & Chemical Engineering 19 (9), 961-981, 1995 | 284 | 1995 |
| Fault detection and classification using artificial neural networks S Heo, JH Lee IFAC-PapersOnLine 51 (18), 470-475, 2018 | 280 | 2018 |
| Receding horizon recursive state estimation KR Muske, JB Rawlings, JH Lee 1993 American Control Conference, 900-904, 1993 | 252 | 1993 |
Matthew RealffGeorgia Institute of TechnologyVerified email at chbe.gatech.edu
