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Permanent link (DOI): https://doi.org/10.7939/R33999

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Identification of Switched Linear Systems Open Access

Descriptions

Other title
Subject/Keyword
Hough Transform
Identification
Switched Linear Systems
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Wang, Jiadong
Supervisor and department
Chen, Tongwen (Electrical and Computer Engineering)
Examining committee member and department
Lynch, Alan (Electrical and Computer Engineering)
Zhao, Qing (Electrical and Computer Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Wu, Fangxiang (Mechanical Engineering)
Prasad, Vinay (Chemical and Material Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Control
Date accepted
2013-04-05T15:46:41Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This thesis is concerned with identification of switched linear systems (SLSs), which is an important part in model-based control. There are a large number of physical systems that can be represented or approximated by SLSs. Therefore, the study of SLSs has attracted much attention over the past decades. As input/output data points of SLSs are sampled from a couple of linear modes (or subsystems), conventional methods are not applicable. For this reason, many research results on identification of SLSs have emerged in recent years. For offline identification of SLSs, many of the existing methods are designed with the assumption that the number of modes is known. This information is, however, not always available in practice. In this thesis, a set membership identification approach is employed to remove this restriction. In its implementation, a major challenge is how to find a maximum feasible subsystem in an efficient way. To achieve this goal, a relaxed heuristic (RH) solution is proposed. Moreover, for SLSs with multiple unknown noise levels, an extended version of the RH solution is subsequently developed. For online identification, a good mode detection or online data classification procedure is critical to estimation performance. One simple and effective way is to directly run a mode detection function before parameter estimation. However, this creates a problem that there may involve a lot of mode mismatches in the mode detection, which has negative impacts on estimation results. In the thesis, two effective algorithms are developed to overcome this problem from different perspectives. In addition to the above aspects, identification of periodically switched linear systems has also been considered in the thesis.
Language
English
DOI
doi:10.7939/R33999
Rights
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.
Citation for previous publication
J. Wang and T. Chen. Online identification of switched linear output error models. In 2011 IEEE International Symposium on Computer-Aided Control System Design (CACSD), pages 1379-1384, Denver, USA, Sept. 2011.J. Wang and T. Chen. Parameter estimation of periodically switched linear systems. IET Control Theory Applications, 6(6):768-775, 12 2012.

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