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Cited by 225

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Stability and persistent disturbance attenuation properties for a class of networked control systems: switched system approach

Lin, H., Antsaklis, P. J.·2005·International Journal of Control

Abstract

In this paper, both the asymptotic stability and l ∞ persistent disturbance attenuation issues are investigated for a class of networked control systems (NCSs) under bounded uncertain access delay and packet dropout effects. The basic idea is to formulate such NCSs as discrete-time switched systems with arbitrary switching. Then the NCSs' stability and performance problems can be reduced to the corresponding problems of such switched systems. The asymptotic stability problem is considered first, and a necessary and sufficient condition is derived for the NCSs' asymptotic stability based on robust stability techniques. Secondly, the NCSs' l ∞ persistent disturbance attenuation properties are studied and an algorithm is introduced to calculate the l ∞ induced gain of the NCSs. The decidability issue of the algorithm is also discussed. A network controlled integrator system is used throughout the paper for illustration.

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Metadata

Authors
Lin, H., & Antsaklis, P. J.
Venue
International Journal of Control
Published
2005-12-15
Volume
78
Issue
18
Pages
1447-1458
ISSN
  • 0020-7179 (ISSN-L)
  • 1366-5820
OpenAlex
W1974953144
Cited by
225
References
0
FWCI
23.68
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stability and persistent disturbance attenuation properties for a class of networked control systems: switched system approach. https://app.cochat.ai/papers/10.1080/00207170500329182
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Lin, H., & Antsaklis, P. J. (2005). Stability and persistent disturbance attenuation properties for a class of networked control systems: switched system approach. International Journal of Control. https://doi.org/10.1080/00207170500329182

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.