Optimal Full-Order and Reduced-Order Estimators for Discrete-Time Systems With Multiple Packet Dropouts

Sun, Shuli; Xie, Lihua; Xiao, Wendong

doi:10.1109/tsp.2008.923196

Cited by 91 publications

(54 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proof: From Theorem 2, we know that there exists an admissible filter in the form of (7) such that the filtering error system (8) is robustly exponentially stable with a guaranteed performance if there exist matrices , , and positive constant scalars satisfying (20), (21). By the Schur complement, (20) is equivalent to (27) Authorized licensed use limited to: Brunel University.…”

Section: Robust Filter Designmentioning

confidence: 99%

“…By the Schur complement, (20) is equivalent to (27) Authorized licensed use limited to: Brunel University. Downloaded on March 10,2010 at 13:24:56 EST from IEEE Xplore.…”

Section: Robust Filter Designmentioning

confidence: 99%

“…Nevertheless, the NCS-related challenging problems arise inevitably that would degrade the system performances. Such network-induced problems include, but are not limited to, the communication delays (also called network-induced time-delays) [3], [7], [10], [22], [23], [30], [35], and packet dropouts (probabilistic information missing, missing measurement) [4], [13], [17], [19], [20], [25]. In most relevant literature, the network-induced time-delays have been commonly assumed to be deterministic, which is fairly unrealistic as, by nature, delays resulting from network transmissions are typically time-varying and random.…”

mentioning

confidence: 99%

“…In most results reported until now, however, it has been implicitly assumed that the measurement signal is either completely available (denoted by 1) or completely missing (denoted by 0), and all the sensors have the same missing probability [8], [22]. Unfortunately, such an assumption cannot cover some practical cases where partial/multiple missing measurements take place for an array of sensors, such as the case when the individual sensor has different missing probability and the case when only partial information is missing [19], [20], [25]. Note that the nonlinear filtering problem has been intensively investigated from researchers for several decades.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Robust $H_{\infty}$ Filtering for a Class of Nonlinear Networked Systems With Multiple Stochastic Communication Delays and Packet Dropouts

Dong

Wang²,

Gao

2010

IEEE Trans. Signal Process.

269

107

View full text Add to dashboard Cite

Abstract-In this paper, the robust filtering problem is studied for a class of uncertain nonlinear networked systems with both multiple stochastic time-varying communication delays and multiple packet dropouts. A sequence of random variables, all of which are mutually independent but obey Bernoulli distribution, are introduced to account for the randomly occurred communication delays. The packet dropout phenomenon occurs in a random way and the occurrence probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval [0 1]. The discrete-time system under consideration is also subject to parameter uncertainties, state-dependent stochastic disturbances and sector-bounded nonlinearities. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of the performance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribed performance for the overall filtering error dynamics, in the presence of random delays, random dropouts, nonlinearities, and the parameter uncertainties. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities (LMIs), and then the explicit expression is given for the desired filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed filter design technique in this paper.Index Terms-Networked systems, nonlinear systems, packet dropout, robust filtering, stochastic systems, stochastic time-varying communication delays.

show abstract

Section: Robust Filter Designmentioning

confidence: 99%

“…By the Schur complement, (20) is equivalent to (27) Authorized licensed use limited to: Brunel University. Downloaded on March 10,2010 at 13:24:56 EST from IEEE Xplore.…”

Section: Robust Filter Designmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Robust $H_{\infty}$ Filtering for a Class of Nonlinear Networked Systems With Multiple Stochastic Communication Delays and Packet Dropouts

Dong

Wang²,

Gao

2010

IEEE Trans. Signal Process.

269

107

View full text Add to dashboard Cite

show abstract

“…Such a missing measurement phenomenon that typically occurs in networked control systems has attracted considerable attention during the past few years, see [4], [9], [10], [12], [13], [19], [20], [23], and the references therein. Up to now, in most reported paper concerning missing measurements, a common assumption is that the measurement signal is either completely missing or completely available, and all the sensors have the same data missing probability [7], [22].…”

mentioning

confidence: 99%

Variance-Constrained ${\cal H}_{\infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

Dong

Wang

et al. 2010

IEEE Trans. Signal Process.

137

View full text Add to dashboard Cite

Abstract-This paper is concerned with the robust H1 finitehorizon filtering problem for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints. All the system parameters are time-varying and the uncertainty enters into the state matrix. The measurement missing phenomenon occurs in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval [0 1]. The stochastic nonlinearities under consideration here are described by statistical means which can cover several classes of well-studied nonlinearities. Sufficient conditions are derived for a finite-horizon filter to satisfy both the estimation error variance constraints and the prescribed H 1 performance requirement. These conditions are expressed in terms of the feasibility of a series of recursive linear matrix inequalities (RLMIs). Simulation results demonstrate the effectiveness of the developed filter design scheme.

show abstract

References

2013

Filtering, Control and Fault Detection With Randomly Occurring Incomplete Information

View full text Add to dashboard Cite

Optimal Full-Order and Reduced-Order Estimators for Discrete-Time Systems With Multiple Packet Dropouts

Cited by 91 publications

References 25 publications

Robust $H_{\infty}$ Filtering for a Class of Nonlinear Networked Systems With Multiple Stochastic Communication Delays and Packet Dropouts

Robust $H_{\infty}$ Filtering for a Class of Nonlinear Networked Systems With Multiple Stochastic Communication Delays and Packet Dropouts

Variance-Constrained ${\cal H}_{\infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

References

Contact Info

Product

Resources

About