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Distributed H∞ moving horizon estimation over energy harvesting sensor networks.

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Title: Distributed H∞ moving horizon estimation over energy harvesting sensor networks.
Authors: Liang, Chaoyang1 (AUTHOR), He, Defeng1 (AUTHOR) hdfzj@zjut.edu.cn, Xu, Chenhui1 (AUTHOR)
Source: International Journal of Systems Science. Nov2025, Vol. 56 Issue 15, p3743-3757. 15p.
Subjects: Energy harvesting, Sensor networks, Nonlinear estimation, Nonlinear systems, Energy industries, Poisson processes, Energy consumption, Estimation theory
Abstract: This paper addresses the distributed $ {H_\infty } $ H ∞ moving horizon estimation problem for nonlinear systems over energy harvesting sensor networks in a deterministic framework, where each sensor is able to gather energy from the surrounding environment. A transformed Poisson process model is introduced to describe the energy collected by each sensor, with particular consideration given to the minimum energy collected. In contrast to previous research on distributed state estimation over energy harvesting sensor networks, which solely considered the energy costs associated with transmission and assumed knowledge of the statistical properties of harvested energy, we consider a more comprehensive scenario. Specifically, we account for the energy costs associated with both sensor sensing and data transmission to neighbouring sensors, while only the parameters of the minimum harvested energy are known. Subsequently, a novel energy allocation strategy is proposed to provide energy for each sensor sensing and transmission based on a predetermined fixed circular order, which determines a maximum time interval for each sensor sensing and transmission. Then, local measurement output predictors and prior state predictors are constructed to handle interruptions in sensor sensing and communication caused by the energy harvesting mechanism. Furthermore, we derive sufficient conditions for the existence of a distributed moving horizon estimator, ensuring $ {H_\infty } $ H ∞ consensus. To illustrate the proposed method's effectiveness, a single-machine infinite-bus power system is presented. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 188363039
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Distributed H<subscript>∞</subscript> moving horizon estimation over energy harvesting sensor networks.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Liang%2C+Chaoyang%22">Liang, Chaoyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Defeng%22">He, Defeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hdfzj@zjut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Chenhui%22">Xu, Chenhui</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Systems+Science%22">International Journal of Systems Science</searchLink>. Nov2025, Vol. 56 Issue 15, p3743-3757. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Energy+harvesting%22">Energy harvesting</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+estimation%22">Nonlinear estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+industries%22">Energy industries</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+processes%22">Poisson processes</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper addresses the distributed $ {H_\infty } $ H ∞ moving horizon estimation problem for nonlinear systems over energy harvesting sensor networks in a deterministic framework, where each sensor is able to gather energy from the surrounding environment. A transformed Poisson process model is introduced to describe the energy collected by each sensor, with particular consideration given to the minimum energy collected. In contrast to previous research on distributed state estimation over energy harvesting sensor networks, which solely considered the energy costs associated with transmission and assumed knowledge of the statistical properties of harvested energy, we consider a more comprehensive scenario. Specifically, we account for the energy costs associated with both sensor sensing and data transmission to neighbouring sensors, while only the parameters of the minimum harvested energy are known. Subsequently, a novel energy allocation strategy is proposed to provide energy for each sensor sensing and transmission based on a predetermined fixed circular order, which determines a maximum time interval for each sensor sensing and transmission. Then, local measurement output predictors and prior state predictors are constructed to handle interruptions in sensor sensing and communication caused by the energy harvesting mechanism. Furthermore, we derive sufficient conditions for the existence of a distributed moving horizon estimator, ensuring $ {H_\infty } $ H ∞ consensus. To illustrate the proposed method's effectiveness, a single-machine infinite-bus power system is presented. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/00207721.2025.2476173
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 3743
    Subjects:
      – SubjectFull: Energy harvesting
        Type: general
      – SubjectFull: Sensor networks
        Type: general
      – SubjectFull: Nonlinear estimation
        Type: general
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Energy industries
        Type: general
      – SubjectFull: Poisson processes
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Estimation theory
        Type: general
    Titles:
      – TitleFull: Distributed H∞ moving horizon estimation over energy harvesting sensor networks.
        Type: main
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          Name:
            NameFull: Liang, Chaoyang
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          Name:
            NameFull: He, Defeng
      – PersonEntity:
          Name:
            NameFull: Xu, Chenhui
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          Dates:
            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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              Value: 56
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              Value: 15
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            – TitleFull: International Journal of Systems Science
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