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Distributed Moving Horizon State Estimation of Nonlinear Systems Open Access


Other title
nonlinear systems
chemical processes
moving horizon estimation
distributed state estimation
Type of item
Degree grantor
University of Alberta
Author or creator
Zhang, Jing
Supervisor and department
Liu, Jinfeng (Department of Chemical and Materials Engineering)
Examining committee member and department
Prasad, Vinay (Department of Chemical and Materials Engineering)
Li, Zukui (Department of Chemical and Materials Engineering)
Liu, Jinfeng (Department of Chemical and Materials Engineering)
Chung, Hyun Joong (Department of Chemical and Materials Engineering)
Department of Chemical and Materials Engineering
Chemical Engineering
Date accepted
Graduation date
Master of Science
Degree level
Large-scale complex chemical processes increasingly appear in the modern process industry due to their economic efficiency. Such a large-scale complex chemical process usually consists of several unit operations (subsystems), which are connected together through material and energy flows. Because of the increased process scale and the significant interactions between different subsystems, it poses great challenges in the design of automatic control systems for such large-scale complex chemical processes which are desired to fulfill the fundamental safety, environmental sustainability and profitability requirements. In recent years, distributed predictive process control has emerged as an attractive control approach to handle the scale and interactions of large-scale complex chemical processes. It has been demonstrated that distributed predictive process control can achieve improved closed-loop performance compared with decentralized control while preserving the flexibility of the decentralized framework. However, almost all of the existing distributed predictive process control designs are developed under the assumption that the state measurements of subsystems or the entire system are available. This assumption does not hold in many applications. This thesis presents a robust distributed moving horizon state estimation (DMHE) scheme that is appropriate for output feedback distributed predictive control of nonlinear systems as well as approaches for reducing the communication demand of the proposed DMHE scheme and a strategy for handling delays in the communication between subsystem estimators. First, the proposed robust DMHE scheme is presented for a class of nonlinear systems that are composed of several subsystems. It is assumed that the subsystems interact with each other via their states only. Subsequently, two triggered communication algorithms are introduced for the proposed DMHE scheme to reduce the number of information transmissions between subsystems. Following this, an approach is proposed to handle the potential time-varying delays in the communication between the subsystem estimators. The applicability and effectiveness of the proposed approaches are illustrated via their applications to chemical process examples.
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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