Robust Finite-horizon l2-l∞ Filtering for Discrete-time Nonhomogeneous Markovian Jump Systems Under Denial-of-service Attacks

He, Quangui; Shi, Zhiwei; Wu, Dingli; Fan, Shisheng; Zhang, Zhengkuan; Liu, Deshuang

doi:10.1007/s12555-021-0415-3

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…[12], Wu et al explored a stochastic MJS and proposed an optimization-based FTF method. He et al [13] established criteria to ensure the existence of finite-time l 2 − l ∞ filters for discrete-time MJSs. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization

Dong 董,

Ma 马,

Zhang 张

et al. 2023

Chinese Phys. B

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This paper is concerned with finite-time H ∞ filtering for Markov jump systems with uniform quantization. The objective is to design quantized mode-dependent filters to ensure that the filtering error system is not only mean-square finite-time bounded but also has a prescribed finite-time H ∞ performance. First, the case where the switching modes of the filter align with those of the MJS is considered. A numerically tractable filter design approach is proposed utilizing a mode-dependent Lyapunov function, Schur’s complement, and Dynkin’s formula. Then, the study is extended to a scenario where the switching modes of the filter can differ from those of the MJS. To address this situation, a mode-mismatched filter design approach is developed by leveraging a hidden Markov model to describe the asynchronous mode switching and the double expectation formula. Finally, a spring system model subject to a Markov chain is employed to validate the effectiveness of the quantized filter design approaches.

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Section: Introductionmentioning

confidence: 99%

“…(ii) In contrast to the works of Refs. [12][13][14], which focused solely on the mode-matched filtering scenario, the present study encompasses both mode-matched and modemismatched filtering cases, and proposes the corresponding filter design approaches.…”

Section: Introductionmentioning

confidence: 99%