Fuzzy based flyback converter

Deepa, K.; Jeyanthi, R.; Mohan, Shweta; Kumar, M. Vijaya

doi:10.1109/icaee.2014.6838477

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…For determining FC dynamics in CCM, a state-space averaging technique was employed [5,24,28]. Inductor current I LM and voltage across the capacitor V C were considered as independent state variables of the state vector X. Thus,…”

Section: Modeling Of Flyback Convertermentioning

confidence: 99%

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Artificial Intelligence-Based Controller for DC-DC Flyback Converter

et al. 2019

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This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

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Section: Modeling Of Flyback Convertermentioning

confidence: 99%

“…Such an FLC can be employed to implement a central action for dealing with the variable structure nature of an FC. In Reference [28], an FLC-based FC is presented where the FLC responds rapidly to changes in load current or input voltage to ensure better load regulation. The controller also offers robustness with a good dynamic response.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Controller for DC-DC Flyback Converter

et al. 2019

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“…In this work, armature voltage control is applied to separately excited DC motor. Speed control can be achieved using various techniques or algorithms such as PID Controller, Fuzzy Logic Controller (FLC), Fuzzy-Genetic Algorithm (FGA), Artificial Neural Networks (ANN) [7], Fuzzy-Ants Colony (FAC). The main motive of this paper is achieving the desired voltage from the PV cell [8] and the boost [9] circuit deploying Single Variable Fuzzy Logic Controller (SFLC) to extract the maximum output power from the motor during different load and environment conditions.…”

Section: Introductionmentioning

confidence: 99%

Electric Motor Drive Using Single Input Fuzzy Logic Controller for Husk Extraction in Rice Mill Industry

Jagadeesh

Deepa

Sireesha

2021

Lecture Notes in Electrical Engineering

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This paper deals with the Single Input Fuzzy Logic Controller (SFLC) deployed for controlling the speed of a DC motor used in Rice mills for separating the husk layer surrounding the paddy to get the rice grain with perfect shape and size. System utilizes the power from the Photo voltaic (PV) array with a boost converter to meet desired specifications of DC motor. Different operating procedures like Proportional Integral Derivative (PID) logic controller procedures can be deployed for the speed control of DC motor. This paper illustrates the fuzzy operation technique with single input variable. PID logic controller requires knotty mathematical models. While Fuzzy Logic Controller (FLC) function is based on rule base knowledge. The DC motor's speed can be tuned to a reasonable extend to function in a settled manner. In summary, this paper demonstrated the speed regulation of both controllers (SFLC and Two input FLC) designed for the DC motor and results are discussed with the help of SIMULINK program in MATLAB.

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