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Finite-Time Nonfragile Dissipative Filter Design for Wireless Networked Systems with Sensor Failures

Author

Listed:
  • R. Sakthivel
  • V. Nithya
  • Yong-Ki Ma
  • Chao Wang

Abstract

In this study, the problem of finite-time nonfragile dissipative-based filter design for wireless sensor networks that is described by discrete-time systems with time-varying delay is investigated. Specifically, to reduce the energy consumption of wireless sensor networks, it is assumed that the signal is not transmitted at each instant and the transmission process is stochastic. By constructing a suitable Lyapunov-Krasovskii functional and employing discrete-time Jensen’s inequality, a new set of sufficient conditions is established in terms of linear matrix inequalities such that the augmented filtering system is stochastically finite-time bounded with a prescribed dissipative performance level. Meanwhile, the desired dissipative-based filter gain matrices can be determined by solving an optimization problem. Finally, two numerical examples are provided to illustrate the effectiveness and the less conservatism of the proposed filter design technique.

Suggested Citation

  • R. Sakthivel & V. Nithya & Yong-Ki Ma & Chao Wang, 2018. "Finite-Time Nonfragile Dissipative Filter Design for Wireless Networked Systems with Sensor Failures," Complexity, Hindawi, vol. 2018, pages 1-13, October.
    • Handle: RePEc:hin:complx:7482015
    DOI: 10.1155/2018/7482015
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    References listed on IDEAS

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    1. M. Sathishkumar & R. Sakthivel & O. M. Kwon & B. Kaviarasan, 2017. "Finite-time mixed and passive filtering for Takagi–Sugeno fuzzy nonhomogeneous Markovian jump systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(7), pages 1416-1427, May.
    2. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    3. Wang, Bo & Yan, Juan & Cheng, Jun & Zhong, Shouming, 2017. "New criteria of stability analysis for generalized neural networks subject to time-varying delayed signals," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 322-333.
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