564 Optimization and Predictive Control


Description:
We seek contributions on new formulations of optimization-based predictive control, as well as novel solution algorithms.

Sponsor:
Systems and Process Control
Co-Sponsor(s):
Computers in Operations and Information Processing (10C)


Chair:
Jin Wang
Auburn University
Chemical Engineering
Auburn, AL
36849
Fax Number: 334-844-2063
Email: wang@auburn.edu

Co-Chair:
Debangsu Bhattacharyya
West Virginia University
Department of Chemical and Biomedical Engineering
Morgantown, WV
Email: Debangsu.Bhattacharyya@mail.wvu.edu


Papers:
564a
NMPC of Semi-Batch Processes under Uncertainty using Pontryagin's Minimum Principle
Erdal Aydin, International Max Planck Research School (IMPRS) for Advanced Methods in Process and Systems Engineering; Dominique Bonvin, Ecole Polytechnique Fédérale de Lausanne; Kai Sundmacher, Max Planck Institute for Dynamics of Complex Technical Systems
564b
Sample-Free Stochastic Nonlinear Model Predictive Control
Joel Paulson, University of California - Berkeley; Vinay Bavdekar, University of California - Berkeley; Ali Mesbah, University of California - Berkeley
564c
Real-Time Optimization Via Modifier Adaptation – on Updating the Model Outputs
Aris Papasavvas, EPFL; Tafarel de Avila Ferreira, EPFL; Alejandro Marchetti, EPFL; Dominique Bonvin, Ecole Polytechnique Fédérale de Lausanne
564d
Nonlinear Model Predictive Control for Zone Tracking
Su Liu, University of Alberta; Jinfeng Liu, University of Alberta
564e
Fast Robust Nonlinear Model Predictive Control Based on Sensitivity Update
Zhou (Joyce) Yu, Carnegie Mellon University; Lorenz T. Biegler, Carnegie Mellon University; Thomas A. Badgwell, ExxonMobil Research & Engineering Company
564f
Fault-Tolerant Economic Model Predictive Control with Empirical Process Models
Anas Alanqar, University of California, Los Angeles; Helen Durand, University of California, Los Angeles; Panagiotis D. Christofides, University of California, Los Angeles
564g
Fault-Tolerant Model Predictive Control of Nonlinear Process Systems Using a Forecast-Triggered Communication Strategy
Da Xue, University of California, Davis; Nael H. El-Farra, University of California, Davis
564h
Multi-Grid Schemes for Multi-Scale Predictive Control
Sungho Shin, University of Wisconsin-Madison; Victor M. Zavala, University of Wisconsin-Madison
Abstract Submissions Closed
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