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Protocol-based H∞ estimation for Markovian jumping delayed systems with partially unknown transition probability

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  • Liu, Guixiu
  • Li, Bing

Abstract

This article pays attention to estimation issue for the state of a particular kind of Markovian jumping delayed systems (MJDSs) with exogenous disturbances. The transition probability of Markovian process is assumed to be partially unknown for accurately reflecting the real complexity of mode switching. To avoid data collision while retaining the necessary requirement of information updating, a scheduling named MEF-TOD protocol is adopted to dynamically allocate access authorization of sensor nodes to estimator. By virtue of binary delta operator, a mode-dependent estimator is built to asymptotically approximate the real state of original system. Through taking a suitable energy functional and exploiting stochastic analysis method, several novel approaches are given to sufficiently make the error asymptotically stable under constraint of H∞ performance. The gain matrices for estimator are ultimately formed through settling a series of inequalities of matrix. At last, a numerical instance exhibits the validity of proposed results.

Suggested Citation

  • Liu, Guixiu & Li, Bing, 2025. "Protocol-based H∞ estimation for Markovian jumping delayed systems with partially unknown transition probability," Applied Mathematics and Computation, Elsevier, vol. 492(C).
    • Handle: RePEc:eee:apmaco:v:492:y:2025:i:c:s0096300324007082
    DOI: 10.1016/j.amc.2024.129247
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    References listed on IDEAS

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    1. Fan Yang & Jiahui Li & Hongli Dong & Yuxuan Shen, 2022. "Proportional–integral-type estimator design for delayed recurrent neural networks under encoding–decoding mechanism," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(13), pages 2729-2741, October.
    2. Chen Gao & Xiao He & Hongli Dong & Hongjian Liu & Guangran Lyu, 2022. "A survey on fault-tolerant consensus control of multi-agent systems: trends, methodologies and prospects," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(13), pages 2800-2813, October.
    3. Meiyu Li & Jinling Liang & Fan Wang, 2022. "Robust set-membership filtering for two-dimensional systems with sensor saturation under the Round-Robin protocol," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(13), pages 2773-2785, October.
    4. Zou, Cong & Li, Bing & Liu, Feiyang & Xu, Bingrui, 2022. "Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delays," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    5. Juanjuan Yang & Lifeng Ma & Yonggang Chen & Xiaojian Yi, 2022. "L2-L∞ state estimation for continuous stochastic delayed neural networks via memory event-triggering strategy," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(13), pages 2742-2757, October.
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