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

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:00134651
DOI:10.1149/1945-7111/ae60a8