Stabilization of linear systems over networks with bounded packet loss

Xiong, Junlin; Lam, James

doi:10.1016/j.automatica.2006.07.017

Cited by 621 publications

(399 citation statements)

References 12 publications

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“…In the same way as in [17], we assume that the packet-loss process is driven by a timehomogeneous Markov chain on a complete probability space (Ω, Ξ, Pr) with finite-state range set M 0 = {0, 1, 2, · · · , s}, where η(i k ) = 0 indicates that there is no packet dropout during the time interval (i k , i k+1 ] and s represents the maximal length of consecutive packet losses. Denote…”

Section: Problem Formulationmentioning

confidence: 86%

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Kalman filtering over unreliable communication networks with bounded Markovian packet dropouts

Xiao

Xie

2008

Intl J Robust & Nonlinear

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SUMMARYWe address the peak covariance stability of time-varying Kalman filter with possible packet losses in transmitting measurement outputs to the filter via a packet-based network. The packet losses are assumed to be bounded and driven by a finite-state Markov process. It is shown that if the observability index of the discrete-time linear timeinvariant(LTI) system under investigation is one, the Kalman filter is peak covariance stable under no additional condition. For discrete LTI systems with observability index greater than one, a sufficient condition for peak covariance stability is obtained in terms of the system dynamics and the probability transition matrix of the Markov chain. Finally, the validity of these results is demonstrated by numerical simulations.

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Section: Problem Formulationmentioning

confidence: 86%

“…For networked systems, control problems with finite-length packet losses have been investigated in [16,17]. Generally, there are two motivations for bounded packet-loss modeling.…”

Section: Introductionmentioning

confidence: 99%

Kalman filtering over unreliable communication networks with bounded Markovian packet dropouts

Xiao

Xie

2008

Intl J Robust & Nonlinear

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“…[1], [10], [11]). In [12] and [13] and stabilization of NCSs with packet dropouts and constant delays are investigated, based on a discrete-time NCS representation. Continuous-time modeling approaches, based on (impulsive) delay-differential equations, including packet dropouts and time-varying delays, are described in [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Stability of networked control systems with large delays

Cloosterman

Wouw

Heemels

et al. 2007

2007 46th IEEE Conference on Decision and Control

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“…Then, by using the lifting technique, the augmented system resulting from (4), (6) and (7) can be written as…”

Section: Model Of the Networked Multi-rate Systemsmentioning

confidence: 99%

Fault detection filter design for networked multi‐rate systems with fading measurements and randomly occurring faults

Yong

Wang

Zou

et al. 2016

IET Control Theory & Applications

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In this paper, the fault detection problem is investigated for a class of networked multi-rate systems (NMSs) with network-induced fading channels and randomly occurring faults. The stochastic characteristics of the fading measurements are governed by mutually independent random channel coefficients over the known interval [0,1]. By applying the lifting technique, the system model for the observer-based fault detection is established. With the aid of the stochastic analysis approach, sufficient conditions are established under which the stochastic stability of the error dynamics for the state estimation is guaranteed and the prescribed H ∞ performance constraint on the error dynamics for the fault estimation is achieved. Based on the established conditions, the addressed fault detection problem of NMSs is recast as a convex optimization one that can be solved via the semi-definite program method, and the explicit expression of the desired fault detection filter is derived by means of the feasibility of certain matrix inequalities. The main results are specialized to the networked single-rate systems that are a special case of the NMSs. Finally, two simulation examples are utilized to illustrate the effectiveness of the proposed fault detection method.

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Stabilization of linear systems over networks with bounded packet loss

Cited by 621 publications

References 12 publications

Kalman filtering over unreliable communication networks with bounded Markovian packet dropouts

Kalman filtering over unreliable communication networks with bounded Markovian packet dropouts

Stability of networked control systems with large delays

Fault detection filter design for networked multi‐rate systems with fading measurements and randomly occurring faults

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