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Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear Systems Open Access


Other title
Communication delay
Discrete communication
Data loss
Distributed estimation
Extended Kalman filter
Interacting subsystems
Adaptive high-gain EKF
Type of item
Degree grantor
University of Alberta
Author or creator
Rashedi, Mohammad
Supervisor and department
Liu, Jinfeng (Chemical and Material Engineering)
Huang, Biao (Chemical and Material Engineering)
Examining committee member and department
Chen, Tongwen (Electrical Engineering)
Rajendran, Arvind (Chemical and Material Engineering)
Choi, Hyo-Jick (Chemical and Material Engineering)
El-Farra, Nael (Chemical Engineering from University of California Davis)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
2017-06:Spring 2017
Doctor of Philosophy
Degree level
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.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
Citation for previous publication
M. Rashedi, J. Liu, and B. Huang. "Distributed adaptive high-gain extended Kalman filtering for nonlinear systems", In proceedings of the 9th International Symposium on Advanced Control of Chemical Processes, volume 48, pages 158{163, Whistler, BC, Canada, 2015.M. Rashedi, J. Liu, and B. Huang. "Communication delays and data losses in distributed adaptive high-gain EKF", AICHE, 62:4321-4333, 2016.

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