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Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear Systems

  • Author / Creator
    Rashedi, Mohammad
  • Recently, increasing attention has been given to the theoretical and practical analysis of large-scale networked systems. Large-scale systems are usually composed of several interconnected subsystems connected through material and energy flows. Due to the scale of these systems and the interactions among subsystems, the design of appropriate process monitoring and control systems is challenging. To handle the scale and interactions of large-scale networked systems in process monitoring and control, distributed predictive control and distributed moving horizon estimation approaches have been developed. The distributed framework can improve the performance of the decentralized network and outperform the centralized framework in terms of fault tolerance. Most of the existing distributed control and process monitoring strategies require the availability of the state measurements of all subsystems; however this requirement may not be satisfied in many applications. In this thesis, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed filters communicate with each other to exchange subsystems' estimates. First, assuming continuous communication among the distributed filters, an implementation strategy which specifies how the distributed filters should communicate is designed and the detailed design of the subsystem filter is described. Second, we consider the case where the subsystem filters communicate to exchange information at discrete-time instants. Following this, the problem of time-varying delays and data losses in communications between subsystems' estimators is considered. For these two latter cases, a state predictor is used in each subsystem filter to provide predictions of the states of other subsystems. Also, to reduce the number of information transmission among the filters and prevent data trafficking, a triggered communication strategy is developed. The stability properties of the proposed distributed estimation schemes with the described communication types are analyzed. Finally, the effectiveness and applicability of the proposed schemes are illustrated via the applications to simulated chemical processes and a Three-Tank experimental system.

  • Subjects / Keywords
  • Graduation date
    2017-06:Spring 2017
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3FN1146X
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Chemical and Materials Engineering
  • Specialization
    • Process Control
  • Supervisor / co-supervisor and their department(s)
    • Liu, Jinfeng (Chemical and Material Engineering)
    • Huang, Biao (Chemical and Material Engineering)
  • Examining committee members and their departments
    • Chen, Tongwen (Electrical Engineering)
    • Choi, Hyo-Jick (Chemical and Material Engineering)
    • El-Farra, Nael (Chemical Engineering from University of California Davis)
    • Rajendran, Arvind (Chemical and Material Engineering)