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Model Predictive Control: Theory and Practice

Model Predictive Control: Theory and Practice

by Jay H. Lee
Professor of Chemical and Biomolecular Engineering at Georgia Tech

Details at http://www.castdiv.org/WebCAST.htm <http://www.castdiv.org/WebCAST.htm>

Deadline to Register- 4 May 2007

DATE: Monday, 7 May 2007, 10am-12noon EST

Dial-in from the comfort of your office to hear the presentation

Abstract
This course will attempt to give the audience a general overview on theories and practice of model predictive control and system identification. Fundamental theories as well as current industrial algorithms and practice will be covered. A short discussion on various system identification methods that are being used to build models for MPC will also be given.

Biographical Sketch
Dr. Lee is currently Director of the Integrated Sensing, System Identification, and Control Laboratory (ISSICL). His group is working on ways to use powerful computers, numerical optimization methods, information processing techniques, and novel sensors to improve the safety and efficiency of chemical and biological processes. The cornerstone of their research is a computer-based optimal control technique called Model Predictive Control (MPC), which has already seen applications on many industrial processes (>3000 worldwide applications) with some impressive results. The main components of MPC are the model, the sensors, and the optimal control algorithm. His research group focuses on integration - rather than mere enhancement of the individual components of MPC. They are developing modeling and system identification tools that allow the user to tailor the modeling efforts to specific end-goals of the control. They are developing techniques for integrating several different types of sensors and a process model so that accurate predictions can be made about the whole system including the behavior of those variables that cannot be measured as frequently or reliably as desired. They are also developing smart control algorithms that make optimal decisions while fully accounting for uncertainties in the model and sensed information. They are conducting a number of fundamental studies on data-assisted modeling, sensing, and control, which are designed to improve the integration step. In addition to the fundamental studies, they are conducting in parallel several application studies involving challenging industrial process control problems, including those that arise in particulate processes, mammalian cell reactors, polymer reactors, simulated moving bed separation systems, and pulp and paper processes.

Dr. Lee received the National Science Foundation's Young Investigator Award and a number of other research and teaching awards. He is also a co-author of the forthcoming book "Model Predictive Control." He is a member of AIChE, IEEE, and ASEE, and participated in organizing several international conferences.


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