Robust H ∞ Filtering for a Class of Nonlinear Discrete-Time Markovian Jump Systems
S. Xu,
T. Chen and
J. Lam
Additional contact information
S. Xu: Nanjing University of Science and Technology
T. Chen: University of Alberta
J. Lam: University of Hong Kong
Journal of Optimization Theory and Applications, 2004, vol. 122, issue 3, No 10, 668 pages
Abstract:
Abstract This paper considers the problem of the robust H ∞ filtering for a class of nonlinear discrete-time Markovian jump systems with real time-varying norm-bounded parameter uncertainty. For each mode, the nonlinearity is assumed to satisfy the global Lipschitz conditions and appears in both the state and measured output equations. The problem that we address is the design of a nonlinear filter which ensures robust stochastic stability and a prescribed H ∞ performance level of the filtering error system for all admissible uncertainties. A sufficient condition for the solvability of this problem is obtained in terms of a set of linear matrix inequalities; an explicit expression of a desired nonlinear H ∞ filter is also given. Finally, an example is provided to demonstrate the effectiveness of the proposed approach.
Keywords: Discrete-time systems; H ∞ filtering; linear matrix inequalities; Markovian jump systems; nonlinear systems; uncertain systems (search for similar items in EconPapers)
Date: 2004
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://link.springer.com/10.1023/B:JOTA.0000042599.46775.a9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:122:y:2004:i:3:d:10.1023_b:jota.0000042599.46775.a9
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2
DOI: 10.1023/B:JOTA.0000042599.46775.a9
Access Statistics for this article
Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull
More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().