Fuzzy Control

Siddique, Nazmul

doi:10.1007/978-3-319-02135-5_5

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…FLC is a formal framework for representing, manipulating, and executing human knowledge about system control without the necessity for a full system mathematical model [38]. A fuzzy controller's basic idea is to enable input-output interactions based on fuzzy rules.…”

Section: C) Fuzzy Logic Controller (Flc) To Control Fess Powermentioning

confidence: 99%

Mitigation of Voltage and Frequency Oscillations of a Microgrid during Wind Gust by Using Fuzzy-Controlled Flywheel Energy Storage

Zobair,

M. Aly,

M. Said

2024

International Journal of Applied Energy Systems

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Optimal operation of distributed energy resources (DERs) becomes one of the most critical aspects in the planning of microgrid systems as a result of energy policies aimed at increasing the integration of renewable energies, lowering fossil fuel use, and reducing the environmental effect. Due to the integration of a significant amount of renewable energy sources (RESs), the system inertia in such microgrids is reduced, which negatively affects frequency and voltage stability and weakens the microgrid. The use of Energy Storage Systems (ESSs) significantly lowers voltage and frequency variations. One of the most popular energy storage devices is the Flywheel Energy Storage System (FESS) which is used in this study to tackle the voltage and frequency variations. Among the benefits of FESS over other ESSs are its low maintenance costs, lengthy service life, lack of pollutants, high energy storage, quick charging, and limitless charge/discharge times. FESS has been controlled using a developed fuzzy logic controller that improves microgrid voltage and frequency. The studied power system model is implemented by the MATLAB/Simulink platform. The impacts of wind speed and load variations are examined in this study where voltage and frequency responses are compared in several scenarios. The proposed method displays a significant improvement in the voltage and frequency of the microgrid system. The obtained outcomes show that the fuzzy-controlled FESS is effective in reducing the voltage and frequency fluctuations of the studied microgrid system and keeping their values within the acceptable international standard limitation.

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Section: C) Fuzzy Logic Controller (Flc) To Control Fess Powermentioning

confidence: 99%

Mitigation of Voltage and Frequency Oscillations of a Microgrid during Wind Gust by Using Fuzzy-Controlled Flywheel Energy Storage

Zobair,

M. Aly,

M. Said

2024

International Journal of Applied Energy Systems

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“…The fuzzy inference sub-process holds knowledge on how best to control the system in the form of a set of rules [11]. Fuzzy rules collect linguistic statements that describe how the Fuzzy Inference System should decide to classify an input or control output.…”

Section: Figure 3 Flow-chart Of Fuzzy Inference Process [7]mentioning

confidence: 99%

Advanced fuzzy rule-based failure mode and effect analysis

Koçak¹,

Tóth-Laufer²,

Pokorádi³

2021

Gradus

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“…Compared with traditional PI methods, the fuzzy control model is based on flexible fuzzy rules and can be used in nonlinear system. However, PD-like fuzzy control model is difficult to achieve high precision, and how to solve the steady-state error is the key to this control model [24]. In this paper, a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI) is proposed.…”

Section: Introductionmentioning

confidence: 99%

FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Wang,

Gu,

et al. 2024

Electronics

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Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control.

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Fuzzy Control

Cited by 5 publications

References 53 publications

Mitigation of Voltage and Frequency Oscillations of a Microgrid during Wind Gust by Using Fuzzy-Controlled Flywheel Energy Storage

Mitigation of Voltage and Frequency Oscillations of a Microgrid during Wind Gust by Using Fuzzy-Controlled Flywheel Energy Storage

Advanced fuzzy rule-based failure mode and effect analysis

FI-NPI: Exploring Optimal Control in Parallel Platform Systems

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