Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/78782
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Type: Journal article
Title: H∞ model reduction for discrete-time Markov jump linear systems with partially known transition probabilities
Other Titles: H infinity model reduction for discrete-time Markov jump linear systems with partially known transition probabilities
Author: Zhang, L.
Boukas, E.
Shi, P.
Citation: International Journal of Control, 2009; 82(2):343-351
Publisher: Taylor & Francis Ltd
Issue Date: 2009
ISSN: 0020-7179
1366-5820
Statement of
Responsibility: 
Lixian Zhang, El-Kébir Boukas and Peng Shi
Abstract: In this art\icle, the H model reduction problem for a class of discrete-time Markov jump linear systems (MJLS) with partially known transition probabilities is investigated. The proposed systems are more general, relaxing the traditional assumption in Markov jump systems that all the transition probabilities must be completely known. A reduced-order model is constructed and the LMI-based sufficient conditions of its existence are derived such that the corresponding model error system is internally stochastically stable and has a guaranteed H performance index. A numerical example is given to illustrate the effectiveness and potential of the developed theoretical results.
Keywords: Markov jump linear systems
H1 model reduction
partially known transition probabilities
linear matrix inequality (LMI)
Rights: © 2009 Taylor & Francis
DOI: 10.1080/00207170802098899
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Electrical and Electronic Engineering publications

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