Event-Triggered Recursive State Estimation for Stochastic Complex Dynamical Networks Under Hybrid Attacks
In this article, the event-based recursive state estimation problem is investigated for a class of stochastic complex dynamical networks under cyberattacks. A hybrid cyberattack model is introduced to take into account both the randomly occurring deception attack and the randomly occurring denial-of-service attack. For the sake of reducing the transmission rate and mitigating the network burden, the event-triggered mechanism is employed under which the measurement output is transmitted to the estimator only when a preset condition is satisfied. An upper bound on the estimation error covariance on each node is first derived through solving two coupled Riccati-like difference equations. Then, the desired estimator gain matrix is recursively acquired that minimizes such an upper bound. Using the stochastic analysis theory, the estimation error is proven to be stochastically bounded with probability 1. Finally, an illustrative example is provided to verify the effectiveness of the developed estimator design method.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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| Enthalten in: |
IEEE transactions on neural networks and learning systems - 34(2023), 3 vom: 15. März, Seite 1465-1477 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Yun [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 10.04.2023 Date Revised 11.04.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/TNNLS.2021.3105409 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM330069551 |
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| 520 | |a In this article, the event-based recursive state estimation problem is investigated for a class of stochastic complex dynamical networks under cyberattacks. A hybrid cyberattack model is introduced to take into account both the randomly occurring deception attack and the randomly occurring denial-of-service attack. For the sake of reducing the transmission rate and mitigating the network burden, the event-triggered mechanism is employed under which the measurement output is transmitted to the estimator only when a preset condition is satisfied. An upper bound on the estimation error covariance on each node is first derived through solving two coupled Riccati-like difference equations. Then, the desired estimator gain matrix is recursively acquired that minimizes such an upper bound. Using the stochastic analysis theory, the estimation error is proven to be stochastically bounded with probability 1. Finally, an illustrative example is provided to verify the effectiveness of the developed estimator design method | ||
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| 700 | 1 | |a Dong, Hongli |e verfasserin |4 aut | |
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