filtering for nonlinear stochastic systems with sensor saturation, quantization and random packet losses

Yang, Wei-Bin; Liu, Ming; Shi, Peng

doi:10.1016/j.sigpro.2011.11.019

Cited by 57 publications

(29 citation statements)

References 29 publications

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“…According to Lemmas 1 and 2, it can be verified that system (12) is input-output stable in mean square. Meanwhile, combining (30) and (25), we have…”

Section: Theorem 1: Suppose System Matricesmentioning

confidence: 99%

“…Sensor saturation could limit the performance of sensor, which could lead to poor performance of the system even cause instability of the whole system. Great effort has been drawn to this problem [30,31]. However, to the best of the authors knowledge, the results on H ∞ filtering considering both senor saturations and intermittent measurements remains relative few, which mainly motivated us for this work.…”

Section: Introductionmentioning

confidence: 99%

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Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

You

Yin

2014

IET Control Theory & Applications

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This study addresses the H ∞ filtering design issue for a class of time-delay Markov jump system with nonlinear characteristics. A stochastic system with sensor saturation and intermittent measurements is considered in the authors study. Random noise depending on state and external-disturbance are also taken into account. A decomposition approach and a bernoulli process are utilised to model the characteristic of sensor saturation and missing measurements, respectively. By transforming the filtering error system into an input-output form, sufficient conditions for the stochastic stability of the system with a prescribed H ∞ level are presented with the help of Scaled Small Gain theorem developed for stochastic systems. Based on the proposed conditions, the rubost filter design approach is proposed. A numerical example is finally provided to demonstrate effectiveness of the proposed approahc. NomenclatureThroughout this paper, R n represents the n-dimensional Euclidean space, R n×m is the set of all n × m real matrices, the superscripts '−1' and 'T', respectively, stand for the matrix inverse and matrix transpose. Sym{A} is the shorten notation for A + A T , and the notation P > 0 (respectively, P ≥ 0), for P ∈ R n×n means that P is real symmetric and positive definite (respectively, semidefinite). The symmetric elements of the symmetric matrix is represented by an asterisk ( * ), and the block-diagonal matrices are denoted by diag{. . .}. G 1 • G 2 means the series connection of mapping G 1 and G 2 . E{·} denotes the expectation operator with respect to probability measure, and for vector x(k), x E2 = E{ ∞ n=0 x(n) 2 } 1 2 .

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“…According to Lemmas 1 and 2, it can be verified that system (12) is input-output stable in mean square. Meanwhile, combining (30) and (25), we have…”

Section: Theorem 1: Suppose System Matricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

You

Yin

2014

IET Control Theory & Applications

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“…Compared to the saturation-free case, the nonlinear feature induced by sensor saturation brings in significant difficulties in designing a filter that can guarantee desired filtering performance. Therefore, in the past decade, the H ∞ filtering problem with sensor saturations has received considerable attention and a number of results have been reported that have offered different ways to handle the sensor saturation phenomenon, see, e.g., [15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Reliable H _∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures

Wang

Shen

Wang

et al. 2015

IET Control Theory & Applications

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This paper is concerned with the reliable H ∞ filtering problem for a class of stochastic spatial-temporal systems with sensor saturations and failures. Different from the continuous-spatial temporal systems, the dynamic behaviour of the system under consideration evolves in a discrete rectangular region. The aim of this paper is to estimate the system states through the measurements received from a set of sensors located in some specified points. In order to cater for more realistic signal transmission process, the phenomena of sensor saturations and sensor failures are taken into account. By using the vector reorganization approach, the spatial-temporal system is first transformed into an equivalent ordinary differential dynamic system. Then, a filter is constructed and a sufficient condition is obtained under which the filtering error dynamics is asymptotically stable in probability and the H ∞ performance requirement is met. Based on the analysis results, the desired reliable H ∞ filter is designed. Finally, an illustrative example is given to show the effectiveness of the proposed filtering scheme.

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“…Since the end of last century, the research of NCS experiences the process of from simple to complex, from single to comprehensive and from special to general. A large number of results have been reported, for instance, system complexity analysis [3,4], quantized dynamic output feedback control [5], observer-based controller design [6], state estimation and stabilization [7], H ∞ control method [8,9], faulttolerant control [10], guaranteed cost control [11], codesign [12], etc.…”

Section: Introductionmentioning

confidence: 99%

H-Infinite Controller Design of Singular Networked Control Systems

Qiu¹,

Shi²

2014

ICA

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This paper investigates the H ∞ controller design method for a class of singular networked control systems (SNCS) based on the singular plant. In view of the network-induced delay less than or equal to a sampling period, finite external disturbance, clock-driven sensors, event-driven controller and actuators as well as impulse behavior and structural instability of singular plants, the H ∞ controller design method of SNCS with state feedback way and dynamic output feedback way is investigated respectively by means of the linear matrix inequality method. The existence condition of H ∞ control law, the solving approaches of H ∞ controller parameters and disturbance attenuation degree are presented. Finally, a simulation example is given to illustrate the effectiveness and feasibility of the presented method.

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filtering for nonlinear stochastic systems with sensor saturation, quantization and random packet losses

Cited by 57 publications

References 29 publications

Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

Reliable H _∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures

H-Infinite Controller Design of Singular Networked Control Systems

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filtering for nonlinear stochastic systems with sensor saturation, quantization and random packet losses

Cited by 57 publications

References 29 publications

Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

Reliable H ∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures

H-Infinite Controller Design of Singular Networked Control Systems

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Reliable H _∞ filtering for stochastic spatial–temporal systems with sensor saturations and failures