Robust adaptive fuzzy control for single-chamber single-population microbial fuel cell

Fu, Li; Fu, Xiuwei; Marrani, Hashem Imani

doi:10.1080/21642583.2020.1870580

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…To achieve the above goals, several studies have been conducted by Hashem Imani and his colleagues [43][44], but each of them has weaknesses. Although the work done in [45] has shown good performance, it will certainly not show good performance at critical times due to the non-resistive nature of the predictive controller.…”

Section: Figure 1 Factors Affecting the Performance Of Mfcmentioning

confidence: 99%

Improving the Performance of Uncertain Disturbed Two Population Microbial Fuel Cell Using Robust Adaptive Sliding Mode Control

2024

JNMES

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This article presents a new control approach for the optimal and reliable operation of the microbial fuel cell, as one of the most important sources of renewable energy. Considering several adverse effects on the performance of the microbial fuel cell, including the effects of uncertainty, disturbance, and of course, taking into account the nonlinear dynamics and the necessary conditions for the stable and optimal operation of the fuel cell, this paper presents a novel robust hybrid approach to its performance management. The innovative approach by using adaptive and sliding mode techniques, by focusing on providing a theoretical solution and not changing the structure of the fuel cell, reduces and suppresses the effects of disturbance and uncertainty of the model, and by guaranteeing the stable and reliable operation of the microbial fuel cell in the Lyapunov concept, provides rapid regulation of system states. The results of simulation and comparison in the MATLAB environment show the efficiency and optimal performance of the fuel cell system under the planned control method from the perspective of improving the transient and permanent response.

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Section: Figure 1 Factors Affecting the Performance Of Mfcmentioning

confidence: 99%

Improving the Performance of Uncertain Disturbed Two Population Microbial Fuel Cell Using Robust Adaptive Sliding Mode Control

2024

JNMES

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“…Demir and Eren 26 proposed particle swarm optimization (PSO) and improved gray wolf optimization (IGWO) optimized adaptive neuro fuzzy inference system (ANFIS) controllers to control the output voltage of the two‐chamber MFC system. Moreover, Fu et al 27 designed an adaptive fuzzy controller to eliminate disturbances for single‐chamber single‐population MFC, and Lian et al 28 designed a fuzzy integral shift mode controller for single chamber MFC by combining integral sliding mode and fuzzy sliding mode controllers.…”

Section: Introductionmentioning

confidence: 99%

Design of intelligence‐based optimized adaptive fuzzy PID controllers for a two chamber microbial fuel cell

Eren

Demir

2022

Asia-Pacific J Chem Eng

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The output voltage of microbial fuel cells (MFCs) needs to be controlled effectively to improve the operating efficiency of MFC system against the load variations, disturbances, and uncertainties. The output voltage control is purposed to provide the MFC with an appropriate amount of fuel required to maintain the stability of output voltage. In the applications, the MFC output voltage can be requested to track the variable reference voltage or to track the constant output voltage under changing conditions such as load change. For this purpose, two adaptive fuzzy PID (FPID) controllers powered by optimization algorithms were designed in this study to keep the output voltage value of the MFC at the desired values. This study extends the previous works by using optimization algorithms, such as particle swarm optimization (PSO) and gray wolf optimization (GWO) algorithms, to adjust fuzzy logic parameters, which are used for tuning Proportional‐Integral‐Derivative (PID) controllers. The optimization‐based adaptive FPID controllers play an active role in efficient, fast, and robust tracking of the reference voltage value with its adaptive structure. The designed adaptive control algorithms were tested under different cases, which involve the variation of the reference voltage and load conditions. The results show that both of these controllers can effectively control the output voltage of a MFC by regulating the flow rate of the fuel. In addition, the performances of the designed controller strategies according to the results obtained vary according to the reference signal. While PSO‐based FPID gives more successful results for positive step changes, GWO‐based FPID gives more successful results for negative step changes. These two control strategies show more efficient, robust, successful, and faster performances compared to the traditional PID controller.

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“…[1][2][3][4] Among these types of sources, fuel cells are of particular importance because they require cheap and abundant available primary resources to generate electricity from the reaction of hydrogen and oxygen, and also have very low environmental pollution. 5,6 Among the fuel cell types, a proton exchange membrane fuel cell (PEMFC) is widely used in stationary applications and transportation due to its fast start-up time, high efficiency, high power density, low operating temperature, and optimal power-to-weight ratio, and is more developed than other fuel cells. 7,8 This cell consists of an electrolyte located between two thin electrodes at the anode and cathode.…”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy reset method for pressure control of proton exchange membrane fuel cell in the presence of uncertainty

Golshan

2021

Intl J of Energy Research

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Summary Proton exchange membrane fuel cell as one of the most progressive and extensively used kinds of fuel cells has an effect character in the creation of renewable energy, and control of its principal modules, including the supply manifold, is one of the objectives of recent research. This study suggests a novel robust structure to regulate the supply manifold pressure at the cathode side in addition to cover the nonlinear behavior of fuel cells and achieve optimal performance. Therefore, this article studies a delayed transfer function model that, in addition to considering the effects of uncertainty and the nonlinear behavior of the supply manifold, has the simplicity necessary for designing an operational control method. The proposed control method also consists of two main parts: One is the reset control method, which is designed to improve the transient response in adjusting the supply manifold pressure, and the other is the fuzzy part, which is planned to tune the reset control method coefficients. The simulation and comparison results show the capabilities of the proposed hybrid method to improve the transient response of the system from different perspectives and stability despite the effects of uncertainty.

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Robust adaptive fuzzy control for single-chamber single-population microbial fuel cell

Cited by 4 publications

References 27 publications

Improving the Performance of Uncertain Disturbed Two Population Microbial Fuel Cell Using Robust Adaptive Sliding Mode Control

Improving the Performance of Uncertain Disturbed Two Population Microbial Fuel Cell Using Robust Adaptive Sliding Mode Control

Design of intelligence‐based optimized adaptive fuzzy PID controllers for a two chamber microbial fuel cell

A novel fuzzy reset method for pressure control of proton exchange membrane fuel cell in the presence of uncertainty

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