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State-Estimation-Based Adaptive Voltage Regulation of Microbial Fuel Cells Under Disturbances.

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Title: State-Estimation-Based Adaptive Voltage Regulation of Microbial Fuel Cells Under Disturbances.
Authors: Fu, Xiuwei1 (AUTHOR), Fu, Li1 (AUTHOR) fuli247012412@126.com, Wang, Jiaqi1 (AUTHOR), Zhao, Guanglei2 (AUTHOR)
Source: Journal of The Electrochemical Society. 2026, Vol. 173 Issue 9, p1-9. 9p.
Subjects: Microbial fuel cells, Voltage control, Lyapunov stability, Adaptive control systems, Fuzzy neural networks, Estimation theory, Feedback control systems
Abstract: Microbial fuel cells (MFCs) enable simultaneous wastewater treatment and electricity generation, but practical operation is often limited by slow electrochemical dynamics, transport-induced delays, external disturbances, and unmeasured internal states, which can lead to poor terminal-voltage regulation under changing loads. Here, we propose a state-estimation-based adaptive control framework for stabilizing the MFC terminal voltage in the presence of time-varying external load conditions. The delayed dynamics are reformulated using a Padé approximation to obtain a control-oriented model. A particle-filter-based estimator, combined with a nonlinear observer, reconstructs key internal states online from noisy voltage measurements. The estimated states are then used by an adaptive fuzzy neural network controller to compensate modeling uncertainties and time-varying disturbances. Closed-loop stability is established using Lyapunov analysis. Numerical simulations under representative load-variation scenarios demonstrate reduced voltage oscillations and shorter settling times than a tuned conventional adaptive fuzzy controller, together with improved disturbance rejection, supporting robust MFC voltage regulation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of The Electrochemical Society is the property of IOP Publishing 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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DbLabel: Engineering Source
An: 193527405
AccessLevel: 6
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PubTypeId: academicJournal
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  Data: State-Estimation-Based Adaptive Voltage Regulation of Microbial Fuel Cells Under Disturbances.
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  Data: <searchLink fieldCode="AR" term="%22Fu%2C+Xiuwei%22">Fu, Xiuwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Li%22">Fu, Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fuli247012412@126.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jiaqi%22">Wang, Jiaqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Guanglei%22">Zhao, Guanglei</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+The+Electrochemical+Society%22">Journal of The Electrochemical Society</searchLink>. 2026, Vol. 173 Issue 9, p1-9. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Microbial+fuel+cells%22">Microbial fuel cells</searchLink><br /><searchLink fieldCode="DE" term="%22Voltage+control%22">Voltage control</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+stability%22">Lyapunov stability</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+neural+networks%22">Fuzzy neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink>
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  Label: Abstract
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  Data: Microbial fuel cells (MFCs) enable simultaneous wastewater treatment and electricity generation, but practical operation is often limited by slow electrochemical dynamics, transport-induced delays, external disturbances, and unmeasured internal states, which can lead to poor terminal-voltage regulation under changing loads. Here, we propose a state-estimation-based adaptive control framework for stabilizing the MFC terminal voltage in the presence of time-varying external load conditions. The delayed dynamics are reformulated using a Padé approximation to obtain a control-oriented model. A particle-filter-based estimator, combined with a nonlinear observer, reconstructs key internal states online from noisy voltage measurements. The estimated states are then used by an adaptive fuzzy neural network controller to compensate modeling uncertainties and time-varying disturbances. Closed-loop stability is established using Lyapunov analysis. Numerical simulations under representative load-variation scenarios demonstrate reduced voltage oscillations and shorter settling times than a tuned conventional adaptive fuzzy controller, together with improved disturbance rejection, supporting robust MFC voltage regulation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of The Electrochemical Society is the property of IOP Publishing 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:
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      – Type: doi
        Value: 10.1149/1945-7111/ae60a8
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 9
        StartPage: 1
    Subjects:
      – SubjectFull: Microbial fuel cells
        Type: general
      – SubjectFull: Voltage control
        Type: general
      – SubjectFull: Lyapunov stability
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Fuzzy neural networks
        Type: general
      – SubjectFull: Estimation theory
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      – SubjectFull: Feedback control systems
        Type: general
    Titles:
      – TitleFull: State-Estimation-Based Adaptive Voltage Regulation of Microbial Fuel Cells Under Disturbances.
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            NameFull: Fu, Xiuwei
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            NameFull: Fu, Li
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            NameFull: Wang, Jiaqi
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            NameFull: Zhao, Guanglei
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            – D: 14
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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