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Coordination Techniques for Distributed Model Predictive Control Open Access


Other title
Modified Pseudo-Model Coordination
Goal Coordination
Coordinated Distributed MPC
Plant-Wide Model Predictive Control
Chance-Constrained Coordinated Distributed MPC
Plant-Wide Nonlinear Model Predictive Control
Coordination Methods
Interaction Prediction Coordination
Type of item
Degree grantor
University of Alberta
Author or creator
Ghafoor Mohseni, Padideh
Supervisor and department
Forbes, J. Fraser (Chemical and Materials Engineering)
Examining committee member and department
Liu, Jinfeng (Chemical and Materials Engineering)
Doucette, John (Mechanical Engineering)
Forbes, J. Fraser (Chemical and Materials Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Elkamel, Ali (Chemical Engineering at the University of Waterloo)
Department of Chemical and Materials Engineering
Chemical Engineering
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Industrial chemical plants are complex, highly integrated systems composed of geographically distributed processing units, linked together by material and energy streams. To ensure efficient operation in such integrated plants, multivariable optimal control methods like MPC are required. Although centralized MPC may provide the best achievable control performance, issues such as lack of flexibility and maintainability make this approach impractical. The general industrial practice to plant-wide MPC is to recognize the distributed structure of the processing units to design a network of decentralized MPCs. Decentralized controllers avoid the disadvantages associated with centralized control at the expense of poorer plant-wide control performance. To improve the performance of decentralized controllers, Distributed MPC (DMPC) methods have become centre of attention in the plant-wide optimal control research community. DMPC methods are divided into two general classes of non-coordinated and coordinated approaches. Coordinated Distributed MPC (CDMPC) networks, which consist of distributed controllers and a coordinator, are able to yield optimal centralized solution under a wide range of conditions. This work addresses systematic development of CDMPC networks for plant-wide MPC of interconnected dynamical processes, by modifying the existing decentralized MPC network and designing coordinator. Goal Coordination, Interaction Prediction Coordination and Modified-Pseudo Model Coordination are the three coordination methods studied in this thesis to alter the network of decentralized linear constrained MPCs into CDMPC network. Convergence accuracy studies are provided for the proposed coordination algorithms. CDMPC networks are also developed to study the impacts of uncertainty on the CDMPC and coordinator design using an individual chance-constrained approach. By modifying the CDMPC and coordinator in the Goal Coordination method, it is shown that choosing efficient numerical strategies can improve convergence performance of the coordination algorithm. A novel linear CDMPC network, which has performance of centralized nonlinear MPC, is presented to address the plant-wide nonlinear MPC problem. Numerical simulations are provided to test performance of the proposed CDMPC networks.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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