Jay H. Lee

发表

Reinforcement learning for batch process control: Review and perspectives

Haeun Yoo, Ha Eun Byun, Dongho Han, 2021, Annu. Rev. Control..

Dynamic penalty function approach for constraints handling in reinforcement learning pdf

Victor M. Zavala, Haeun Yoo, Jay H. Lee, 2021, IFAC-PapersOnLine.

A moving horizon‐based approach for least‐squares estimation

Jay H. Lee, J. Rawlings, Douglas G. Robertson, 1996 .

Receding Horizon Recursive State Estimation

Jay H. Lee, J. Rawlings, K. Muske, 1993, 1993 American Control Conference.

Control-relevant design of periodic test input signals for iterative open-loop identification of multivariable FIR systems

Jay H. Lee, 2003 .

Model predictive control: past, present and future

Jay H. Lee, Manfred Morari, Jay H. Lee, 1999 .

Optimal feedback control strategies for state-space systems with stochastic parameters

B. L. Cooley, Jay H. Lee, 1998, IEEE Trans. Autom. Control..

Model predictive control for nonlinear batch processes with asymptotically perfect tracking

Jay H. Lee, K. Lee, 1997 .

Dynamically scheduled MPC of nonlinear processes using hinging hyperplane models

Jay H. Lee, B. Ogunnaike, Y. Chikkula, 1998 .

Diagnostic Tools for Multivariable Model-Based Control Systems

Jay H. Lee, P. Kesavan, 1997 .

Robust model predictive control of nonlinear systems using input-output models

Jay H. Lee, B. Ogunnaike, Y. Chikkula, 1995, Proceedings of 1995 American Control Conference - ACC'95.

Stability of constrained linear moving horizon estimation

C. V. Rao, Jay H. Lee, J. Rawlings, 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

A least squares formulation for state estimation

Jay H. Lee, D. Robertson, 1995 .

Gray-Box Model Identification of an Integrated Plant with Recycle

Jay H. Lee, T. Tosukhowong, 2008 .

Worst-case formulations of model predictive control for systems with bounded parameters

Zhenghong Yu, Jay H. Lee, Jay H. Lee, 1997, Autom..

State-space interpretation of model predictive control

Manfred Morari, Jay H. Lee, Carlos E. Garcia, 1994, Autom..

On interfacing model predictive controllers with a real-time optimizer

Jong Min Lee, Jay H. Lee, Joseph Z. Lu, 2003 .

Least Squares Formulation of State Estimation

Jay H. Lee, Douglas G. Robertson, 1994 .

ITERATIVE LEARNING CONTROL APPLIED TO BATCH PROCESSES: AN OVERVIEW

Jay H. Lee, Kwang S. Lee, Jay H. Lee, 2006 .

A Technique for Integrated Quality Control, Profile Control, and Constraint Handling for Batch Processes

Jay H. Lee, K. Lee, Insik Chin, 2000 .

Control of Wafer Temperature Uniformity in Rapid Thermal Processing Using an Optimal Iterative Learning Control Technique

Jay H. Lee, K. Lee, Insik Chin, 2001 .

Repetitive model predictive control applied to a simulated moving bed chromatography system

Jay H. Lee, Seshatre Natarajan, Jay H. Lee, 2000 .

Fault diagnosis using the hybrid method of signed digraph and partial least squares with time delay : The pulp mill process

Jay H. Lee, Chonghun Han, T. Tosukhowong, 2006 .

Nonlinear model predictive control of the Tennessee Eastman challenge process

JayHyung Lee, Nl Ricker, Jay H. Lee, 1995 .

Limitations of dynamic matrix control

Sigurd Skogestad, Manfred Morari, P Lundstrom, 1995 .

Approximate dynamic programming approach for process control

Wee Chin Wong, Jay H. Lee, 2009 .

Choice of approximator and design of penalty function for an approximate dynamic programming based control approach

Jay H. Lee, Jong Min Lee, N. Kaisare, 2006 .

Synthesis of run-to-run repetitive control methods using finite impulse response models

Jay H. Lee, K. Lee, W. Won, 2009 .

EMPIRICAL RESULTS ON CONVERGENCE AND EXPLORATION IN APPROXIMATE POLICY ITERATION

Jay H. Lee, J. M. Lee, N. Kaisare, 2005 .

SIMULATION-BASED OPTIMIZATION FOR NONLINEAR OPTIMAL CONTROL

Jay H. Lee, J. M. Lee, N. Kaisare, 2002 .

Hydrogen generation in a reverse‐flow microreactor: 1. Model formulation and scaling

Andrei G. Fedorov, Niket S. Kaisare, Jay H. Lee, 2005 .

Robust forecasts and run-to-run control for processes with linear drifts

Choon Meng Kiew, Jay H. Lee, C. Kiew, 2009 .

Identification and Control of Processes with Periodic Operations or Disturbances

Yangdong Pan, Jay H. Lee, 2003 .

Robust Adaptive Predictive Control of Nonlinear Processes Using Nonlinear Moving Average System Models

Yugender Chikkula, Jay H. Lee, 2000 .

Recent Advances and Challenges in Process Identification

Sten Bay Jørgensen, Jay H. Lee, S. Jørgensen, 2002 .

Optimal design of periodic test input signals for multivariable impulse response models

Niket S. Kaisare, Jay H. Lee, N. Kaisare, 2010 .

Pseudo-random binary sequence design for finite impulse response identification

Su Whan Sung, Jay H. Lee, S. Sung, 2001 .

Fault detection and diagnosis of pulp mill process

Thidarat Tosukhowong, Jay H. Lee, Gibaek Lee, 2006 .

Hydrogen generation in a reverse‐flow microreactor: 2. Simulation and analysis

Andrei G. Fedorov, Niket S. Kaisare, Jay H. Lee, 2005 .

Postdecision-State-Based Approximate Dynamic Programming for Robust Predictive Control of Constrained Stochastic Processes

Wee Chin Wong, Jay H. Lee, 2011 .

Simulation based strategy for nonlinear optimal control: application to a microbial cell reactor

Niket S. Kaisare, Jong Min Lee, Jay H. Lee, 2003 .

Monitoring of batch processes through state‐space models

Andrew W. Dorsey, Jay H. Lee, 2004 .

Fault Detection and Diagnosis Using Hidden Markov Disturbance Models

Wee Chin Wong, Jay H. Lee, 2010 .

On interfacing model predictive controllers with low-level loops

Sunwon Park, Yongho Lee, Jay H. Lee, 1999 .

Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

Fengqi You, Jay H. Lee, Shuwen Lu, 2020 .

Selective intermittent adaptive control of processes subject to large infrequent changes

Jay H. Lee, P. Kesavan, 2000 .

Approximate dynamic programming based optimal control applied to an integrated plant with a reactor and a distillation column with recycle

Thidarat Tosukhowong, Jay H. Lee, 2009 .

Synthesis of self-triggered receding horizon controllers: A relaxed dynamic programming approach

Liang Lu, Jay H. Lee, Liang Lu, 2015, 2015 34th Chinese Control Conference (CCC).

LMI-based optimal design of test signals for control-relevant system identification

Y. Samyudia, Jay H. Lee, 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

Improving computational efficiency of model predictive control algorithm using wavelet transformation

Jeffrey C. Kantor, Yugender Chikkula, Jay H. Lee, 1995 .

Bilevel Optimizing Control Structure for a Simulated Moving Bed Process Based on a Reduced-Order Model Using the Cubic Spline Collocation Method

Kwang Soon Lee, Jay H. Lee, Kiwoong Kim, 2010 .

Value function-based approach to the scheduling of multiple controllers

Jong Min Lee, Jay H. Lee, J. M. Lee, 2008 .

State-Estimation-Based Model Predictive Control with On-Line Robustness Tuning Parameters

Manfred Morari, Carlos E. Garcia, Jay H. Lee, 1991, 1991 American Control Conference.

Approximate dynamic programming strategy for dual adaptive control

Jong Min Lee, Jay H. Lee, 2005 .

Design of quadratic criterion-based iterative learning control

Kwang Soon Lee, Jay H. Lee, 1998 .

A dynamic penalty approach to state constraint handling in deep reinforcement learning

Jay H. Lee, V. Zavala, Haeun Yoo, 2022, Journal of Process Control.

Special issue on process dynamics and control

Jay H. Lee, S. Joe Qin, En-Sup Yoon, 2003 .

Linear Model Predictive Control in the Process Industries

Manfred Morari, Jay Lee, Jay H. Lee, 2010 .

Nonlinear Model Predictive Control of Post-combustion CO2 Capture Process for Flexible Operation

Jay H. Lee, Ho-Jeon Jung, 2022, IFAC-PapersOnLine.

Control structure selection: issues and a new methodology

Jay H. Lee, Manfred Morari, Jay H. Lee, 1990 .

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 .

From robust model predictive control to stochastic optimal control and approximate dynamic programming: A perspective gained from a personal journey

Jay H. Lee, Jay H. Lee, 2014, Comput. Chem. Eng..

Approximate Dynamic Programming Strategies and Their Applicability for Process Control: A Review and Future Directions

Jong Min Lee, Jay H. Lee, Jay H. Lee, 2004 .

Intelligent control of dynamic systems

T. I. Liu, E. J. Ko, J. Lee, 1993 .

Control-relevant experiment design for multivariable systems described by expansions in orthonormal bases

Jay H. Lee, Brian L. Cooley, B. L. Cooley, 2001, Autom..

Tuning of model predictive controllers for robust performance

Jay H. Lee, Z. H. Yu, Jay H. Lee, 1994 .

A model-based predictive control approach to repetitive control of continuous processes with periodic operations

Jay H. Lee, Kwang S. Lee, Seshatre Natarajan, 2001 .

Fault Detection in Process Systems using Hidden Markov Disturbance Models

Wee Chin Wong, Jay H. Lee, Jay H. Lee, 2009 .

Improved disturbance and fault signal modeling via Hidden Markov Models

Wee Chin Wong, Jay H. Lee, Jay H. Lee, 2011, 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP).

Realistic disturbance modeling using Hidden Markov Models: Applications in model-based process control

Jay H. Lee, Wee Chin Wong, Jay H. Lee, 2009 .

A method for the estimation of infrequent abrupt changes in nonlinear systems

Jay H. Lee, Douglas G. Robertson, Jay H. Lee, 1998, Autom..

Progress and challenges in control of chemical processes.

Jong Min Lee, Jay H Lee, Jay H. Lee, 2014, Annual review of chemical and biomolecular engineering.

Simulation-based learning of cost-to-go for control of nonlinear processes

Jong Min Lee, Jay H. Lee, Jay H. Lee, 2004 .

Neuro-dynamic programming method for MPC 1

Jay H. Lee, Jong Min Lee, Jay H. Lee, 2001 .

Real-time control of photoresist development process

Arthur Tay, Weng Khuen Ho, Jay H. Lee, 2005, SPIE Advanced Lithography.

Screening plant designs and control structures for uncertain systems

Richard D. Braatz, Jay H. Lee, Manfred Morari, 1994 .

Approximate dynamic programming based approach to process control and scheduling

Jay H. Lee, Jong Min Lee, Jay H. Lee, 2006, Comput. Chem. Eng..

Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation

Haeun Yoo, Jay H. Lee, Boeun Kim, 2021, Comput. Chem. Eng..

Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables

Jay H. Lee, Kwang S. Lee, Jay H. Lee, 2003 .

Period-robust repetitive model predictive control

Jay H. Lee, Manish Gupta, Jay H. Lee, 2006 .

Convergence of constrained model-based predictive control for batch processes

Jay H. Lee, Kwang S. Lee, Jay H. Lee, 2000, IEEE Trans. Autom. Control..

Model-based iterative learning control with a quadratic criterion for time-varying linear systems

Jay H. Lee, Kwang S. Lee, Won C. Kim, 2000, Autom..

Model predictive control technique combined with iterative learning for batch processes

Jay H. Lee, Kwang S. Lee, In‐Shik Chin, 1999 .

Simulation-based design of dual-mode controller for non-linear processes

Jong Min Lee, Jay H. Lee, Jay H. Lee, 2008 .

A Hidden Markov disturbance model for Offset-free linear model predictive control

Jay H. Lee, Wee Chin Wong, Jay H. Lee, 2008 .

Constrained linear state estimation - a moving horizon approach

Jay H. Lee, James B. Rawlings, Christopher V. Rao, 2001, Autom..

Integrated Identification and Robust Control

Jay H. Lee, Brian L. Cooley, B. L. Cooley, 1997 .

Control relevant identification of ill-conditioned systems: Estimation of gain directionalyty

Wei Li, Jay H. Lee, Jay H. Lee, 1996 .

Screening tools for robust control structure selection

Richard D. Braatz, Manfred Morari, Jay H. Lee, 1995, Autom..

An approximate dynamic programming based approach to dual adaptive control

Jong Min Lee, Jay H. Lee, Jay H. Lee, 2009 .

Reducing the computational effort of optimal process controllers for continuous state spaces by using incremental learning and post-decision state formulations☆

Norbert Link, Jay H. Lee, Jürgen Pollak, 2014 .

An introduction to a dynamic plant-wide optimization strategy for an integrated plant

Jay H. Lee, Jong Min Lee, Joseph Lu, 2004, Comput. Chem. Eng..

Real-time economic optimization for an integrated plant via a dynamic optimization scheme

J.H. Lee, T. Tosukhowong, Jay H. Lee, 2004, Proceedings of the 2004 American Control Conference.

Nonlinear modeling and state estimation for the Tennessee Eastman challenge process

JayHyung Lee, Nl Ricker, Jay H. Lee, 1995 .

Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes

Jay H. Lee, Jong Min Lee, Jay H. Lee, 2005, Autom..

Extended Kalman Filter Based Nonlinear Model Predictive Control

Jay H. Lee, N. Lawrence Ricker, Jay H. Lee, 1993, 1993 American Control Conference.

On the use of constraints in least squares estimation and control

Jay H. Lee, Douglas G. Robertson, Jay H. Lee, 2002, Autom..

Subspace identification based inferential control applied to a continuous pulp digester

Jay H. Lee, Raja Amirthalingam, Jay H. Lee, 1999 .

Nonlinear inferential control of pulp digesters

JayHyung Lee, Ak Datta, Jay H. Lee, 1994 .

Recursive data-based prediction and control of product quality for a PMMA batch process

Jay H. Lee, Yangdong Pan, Jay H. Lee, 2003 .

Recursive data-based prediction and control of product quality for a batch PMMA reactor

Jay H. Lee, Yangdong Pan, Jay H. Lee, 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

Robust inferential control of multi-rate sampled-data systems

Jay H. Lee, Manfred Morari, Jay H. Lee, 1992 .

Control structure selection and robust control system design for a high-purity distillation column

Manfred Morari, Jay H. Lee, Parthasarathy Kesavan, 1997, IEEE Trans. Control. Syst. Technol..

Approximate dynamic programming approach for process control

J H Lee, Jay H. Lee, J. H. Lee, 2010, ICCAS 2010.

A set based approach to detection and isolation of faults in multivariable systems

Jay H. Lee, Parthasarathy Kesavan, Jay H. Lee, 2001 .

Hybrid cybernetic model-based simulation of continuous production of lignocellulosic ethanol: Rejecting abruptly changing feed conditions

Jay H. Lee, Doraiswami Ramkrishna, Wee Chin Wong, 2010 .

Modified subspace identification for long-range prediction model for inferential control

Jay H. Lee, Yangdong Pan, Jay H. Lee, 2008 .

A reinforcement learning-based scheme for adaptive optimal control of linear stochastic systems

J.H. Lee, Wee Chin Wong, Jay H. Lee, 2008, 2008 American Control Conference.

Successive Linearization-Based Stochastic Repetitive Control Technique and Its Applications to SMB a

Kwang Soon Lee, Woohyun Yun, Wangyun Won, 2008 .

A generic framework for integrated quality and profile control for industrial batch processes

Jay H. Lee, Kwang S. Lee, Jay H. Lee, 2001 .

Robust measurement selection

Manfred Morari, Jay H. Lee, Jay H. Lee, 1991, Autom..

Optimal control of a fed-batch bioreactor using simulation-based approximate dynamic programming

Jay H. Lee, Niket S. Kaisare, Catalina Valencia Peroni, 2005, IEEE Transactions on Control Systems Technology.

Empirical model based control of nonlinear processes using approximate dynamic programming

Jay H. Lee, J.H. Lee, J. M. Lee, 2004, Proceedings of the 2004 American Control Conference.

A Phase Segmentation Approach for Applying Reinforcement Learning to Batch Polymerization Process Control

Jay H. Lee, Boeun Kim, Haeun Yoo, 2020 .