Extremum-seeking control of a microbial fuel cell power using adaptive excitation

Kebir, Anouer; Akhrif, Ouassima; Woodward, Lyne

doi:10.1109/iecon.2016.7793789

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…2a). As has shown before in [7]- [9], R l that maximizes (2) depends on S. An analogous derivation results in a similar optimization problem for a MEC, specified using a model from [18].…”

Section: Maximizing Mfc Power and Optimizing Mec Organic Productionsupporting

confidence: 54%

“…It is important to note that all reported convergence times in the table are assessed on the same MFC model, given by [16]. The classical method of perturb & observe converges extremely slow (±100 days) due to the inherent slow dynamics of a MFC [9]. This method however achieves a good precision (< 1%) and does not require any a priori calibration nor extra sensors besides the ones for calculating the optimization function J.…”

Section: Discussionmentioning

confidence: 99%

“…Although this method is straightforward to implement, convergence speed is inverse proportional to accuracy and convergence takes very long in practical settings. More advanced methods [7]- [9] adapt the integrator gain based on a priori available calibration knowledge between the impact of the environment conditions and the maximum producible power. In [7], a second order relation is extracted between the inlet substrate concentration and the maximum power while [8] and [9] use a neural network (NN).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Successive parabolic interpolation as extremum seeking control for microbial fuel & electrolysis cells

Molderez

Wit

Rabaey

et al. 2017

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

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Microbial Fuel Cell (MFC) power production and Microbial Electrolysis Cell (MEC) organic production depend strongly on their dynamic environment conditions, like inlet substrate concentration, temperature, etc. This work presents a discrete extremum seeking controller to quickly tune the MFC and MEC electrical settings in order to achieve maximum performance irrespective of these dynamic environment conditions using the successive parabolic interpolation iteration scheme. The controller converges in about 3.5 days within 5% of the cell's maximum performance and in about 5.4 days within 1% for an established MFC model. The proposed discrete parabola controller converges 3x faster than the state-of-theart controllers without requiring a time-consuming calibration procedure. Equally fast convergence speed is achieved on a MEC model.

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“…2a). As has shown before in [7]- [9], R l that maximizes (2) depends on S. An analogous derivation results in a similar optimization problem for a MEC, specified using a model from [18].…”

Section: Maximizing Mfc Power and Optimizing Mec Organic Productionsupporting

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Successive parabolic interpolation as extremum seeking control for microbial fuel & electrolysis cells

Molderez

Wit

Rabaey

et al. 2017

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

show abstract

“…Indeed, if the system is subject to large and rapid external disturbances, these performance criteria will be degraded in the case of optimization of non-linear systems with unknown dynamics as previously shown in a microbial fuel cell application [4]. Also in [13], for the same system, the effect of the choice of and on ESC performance in terms of accuracy and speed of convergence is shown. Therefore, adapting the ESC parameters, as proposed in this paper, is essential to ensure stability and converge quickly and accurately to the optimal operating point in the presence of external disturbances.…”

Section: Extremum Seeking Controlmentioning

confidence: 98%

“…It has already been applied in simulations to a microbial fuel cell (MFC) model which is characterized by slower and much more complex dynamics than the one for PV systems. In [13], the ESC allowed the MFC system to converge towards the optimum with high accuracy and to gain days in terms of convergence time compared to the conventional ESC.…”

Section: Experimental Study: Application To Pv Systemmentioning

confidence: 99%

Extremum-Seeking Control With Adaptive Excitation: Application to a Photovoltaic System

Kebir

Woodward

Akhrif

2018

IEEE Trans. Ind. Electron.

Self Cite

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-The objective of this study is to improve the performance of the extremum-seeking control ( ) technique in terms of time and accuracy of convergence towards the optimum operating point of a dynamic system subject to the effect of external disturbances. More precisely, the idea is to reduce the undesirable effect of time scale separation in on the performance of the closed loop system. The method consists in adaptively controlling the excitation signal amplitude using a neural network (NN) model, which gives a real-time estimate of the optimal operating point based on the measurement of the external disturbances. Stability of the proposed with adaptive excitation, referred to in the following as , is demonstrated. The superiority of compared to in terms of accuracy and time of convergence to the optimum is demonstrated both theoretically and experimentally, in the case of the optimization of a photovoltaic panel system (PV).

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Dynamic analysis and multiple model control of continuous microbial fuel cell (CMFC)

Yewale

Methekar

Agrawal

2019

Chemical Engineering Research and Design

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Extremum-seeking control of a microbial fuel cell power using adaptive excitation

Cited by 5 publications

References 12 publications

Successive parabolic interpolation as extremum seeking control for microbial fuel & electrolysis cells

Successive parabolic interpolation as extremum seeking control for microbial fuel & electrolysis cells

Extremum-Seeking Control With Adaptive Excitation: Application to a Photovoltaic System

Dynamic analysis and multiple model control of continuous microbial fuel cell (CMFC)

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