scholarly journals A study of Comparative analysis of fuzzy logic controller and neural network for dc–dc buck converter

2021 ◽  
Vol 309 ◽  
pp. 01021
Author(s):  
Shaik Gousia begum ◽  
Syed Sarfaraz Nawaz
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Input Voltage ◽  
Buck Converter ◽  
Major Drawback ◽  
And Performance ◽  
Linear Controllers

This paper presents the comparative analysis between fuzzy logic controller and neural network for DC-DC Buck converter. The major drawback in the conventional buck converter is when the input voltage or load change, the output voltage also changes which reduces the overall efficiency of the buck converter. So here we are using non linear controllers for buck converter which respond quickly for perturbations and maintains the fixed load voltage even when there are non-linearity’s occurs compared to a linear controllers like P,PI,PID controllers which can’t withstand when perturbations occur. Simplicity, low cost and adaptability to the complex systems without mathematical modeling are the best features of Fuzzy Logic controller and neural networks. The Two implementations are analyzed in detail and simulated in MATLAB/SIMULINK environment and results presented. Proposed approach is implemented on DC to DC step down converter for an input of 230V and performance characteristics like maximum overshoot, settling time and efficiency of the converter are studied.

scholarly journals Implementation of Fuzzy Logic Controller for DC–DC step Down Converter

2021 ◽  
Vol 10 (8) ◽  
pp. 109-112
Author(s):  
Shaik Gousia Begum ◽  
Syed Sarfaraz Nawaz ◽  
G. Sai Anjaneyulu
Keyword(s):  
Fuzzy Logic ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Supply Voltage ◽  
Buck Converter ◽  
Buck Converters ◽  
Major Drawback ◽  
Dc Voltage ◽  
Overall Efficiency ◽  
Step Down

This paper presents the design of a Fuzzy logic controller for a DC-DC step-down converter. Buck converters are step-down regulated converters which convert the DC voltage into a lower level standardized DC voltage. The buck converters are used in solar chargers, battery chargers, quadcopters, industrial and traction motor controllers in automobile industries etc. The major drawback in buck converter is that when input voltage and load change, the output voltage also changes which reduces the overall efficiency of the Buck converter. So here we are using a fuzzy logic controller which responds quickly for perturbations, compared to a linear controllers like P, PI, PID controllers. The Fuzzy logic controllers have become popular in designing control application like washing machine, transmission control, because of their simplicity, low cost and adaptability to complex systems without mathematical modeling So we are implementing a fuzzy logic controller for buck converter which maintains fixed output voltage even when there are fluctuations in supply voltage and load. The fuzzy logic controller for the DC-DC Buck converter is simulated using MATLAB/SIMULINK. The proposed approach is implemented on DC-DC step down converter for an input of 230V and we get the desired output for variations in load or references. This proposed system increases the overall efficiency of the buck converter.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

Fuzzy Hybrid Controller Model for Making Decision to Interpret Any Condition

2006 ◽  
Vol 111 ◽  
pp. 167-170
Author(s):  
M. Shahidul Karim ◽  
Rashed Mustafa
Keyword(s):  
Fuzzy Logic ◽  
Control Strategy ◽  
Room Temperature ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Electronic System ◽  
Comfort Level ◽  
Price Ratio ◽  
Hybrid Controller ◽  
Software Development System

The constantly increasing performance/price ratio of microcontrollers means electronic system can replace more and more electromechanical ones. In design, the goal is not to just replace the solution but also to improve it by adding new functionalities. The paper presents a model of industrial controller having possibility of the classical programming controller, with added elements of the fuzzy logic. Here fuzzy logic offers a technical control strategy that uses elements of everyday language. In this application, it is used to design a control strategy that adapts to the need of individual user. It achieves a higher comfort level and reduces energy consumption. Here we have used a fuzzy method which selects the contractions that best meet the specifications, where human knowledge is involved in a decision making process. With a fuzzy-logic software development system, the entire system, which includes conventional code for signal preprocessing as well as the fuzzy logic system, can be implemented on an industry-standard microcontroller. Using fuzzy logic on such a low-cost platform makes this a possible solution with most AC systems. Each home AC has a sensor that measures room temperature and compares it with the temperature set on the dial. The fuzzy logic controller uses a bimetallic switch and compares the set temperature with room temperature.

Low Cost Engine Management System (EMS) for the Cost Sensitive Two-Wheeler Application: Idle Speed and A/F Ratio Control Using PID and Fuzzy Logic Control Algorithms

2007 ◽  
Author(s):  
P. V. Manivannan ◽  
A. Ramesh
Keyword(s):  
Fuzzy Logic ◽  
Fuel Consumption ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Engine Management System ◽  
Position Sensor ◽  
Idle Speed ◽  
The Cost ◽  
Two Wheeler ◽  
Speed Fluctuations

In this work an Engine Management System (EMS) using a low cost 8-bit microcontroller specifically for the cost sensitive small two-wheeler application was designed and developed. Only the Throttle Position Sensor (TPS) and the cam position sensor (also used for speed measurement) were used. A small capacity 125CC four stroke two-wheeler was converted into a Port Fuel Injected (PFI) engine and was coupled to a fully instrumented Eddy Current Dynamometer. Air-fuel ratio was controlled using the open loop, lookup-table [speed (N) and throttle (α)] based technique. Spark Time was controlled using a proportional / fuzzy logic based close loop control algorithm for the idle speed control to reduce fuel consumption and emissions. Test results show a significant improvement in engine performance over the original carbureted engine, in terms of fuel consumption, emissions and idle speed fluctuations. The Proportional controller resulted in significantly lower speed fluctuations and HC / CO emissions than the fuzzy logic controller. Though the fuzzy logic controller resulted in low cycle by cycle variations than the original carbureted engine, it leads to significantly higher HC levels. The performance fuzzy logic can be improved by modifying the membership function shapes with more engine test data.

scholarly journals Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor

2020 ◽  
Vol 17 (3) ◽  
pp. 1289
Author(s):  
Nor Hana Mamat ◽  
Samsul Bahari Mohd Noor ◽  
Laxshan A/L Ramar ◽  
Azura Che Soh ◽  
Farah Saleena Taip ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dissolved Oxygen ◽  
Network Model ◽  
Neural Network Model ◽  
Fermentation Process ◽  
Fuzzy Logic Controller ◽  
Rbf Neural Network ◽  
Aeration Rate ◽  
Process Data

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate.  Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

scholarly journals An intelligent lead-acid battery closed-loop charger using a combined fuzzy controller for PV applications

2021 ◽  
Vol 297 ◽  
pp. 01033
Author(s):  
Iliass Rkik ◽  
Mohamed El khayat ◽  
Hafsa Hamidane ◽  
Abdelali Ed-Dahhak ◽  
Mohammed Guerbaoui ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Control Unit ◽  
Buck Converter ◽  
Lead Acid Battery ◽  
Charging System ◽  
Pv Array ◽  
Full Power ◽  
Lead Acid

This paper presents the modeling of an intelligent combined MPPT and Lead-Acid battery charger controller for standalone solar photovoltaic systems. It involves the control of a DC/DC buck converter through a control unit, which contains two cascaded fuzzy logic controllers (FLC), that adjusts the required duty cycle of the converter according to the state of charge and the three stage lead acid battery charging system. The first fuzzy logic controller (FLC1) consists of an MPPT controller to extract the maximum power produced by the PV array, while the second fuzzy controller (FLC2) is aimed to control the voltage across the battery to ensure the three stage charging approach. This solution of employing two distinct cascaded fuzzy controllers surmounts the drawbacks of the classical chargers in which the voltage provided to the lead acid battery is not constant owing to the effects of the MPPT control which can automatically damage the battery. Thus, the suggested control strategy has the benefit of extracting the full power against the PV array, avoiding battery damage incurred by variable MPPT voltage and increasing the battery’s lifespan.

scholarly journals Fuzzy Logic Based Controller For Buck Converter

2019 ◽  
Vol 1 (3) ◽  
pp. 1-12
Author(s):  
Habibullah Salim ◽  
Irma Husnaini ◽  
Asnil Asnil
Keyword(s):  
Fuzzy Logic ◽  
Solar Cell ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Pulse Generation ◽  
Buck Converter ◽  
Dc Voltage ◽  
Generation Technique ◽  
Fuzzy Logic Method ◽  
The Stability

This research aims to make buck converter prototype for PLTS system by using fuzzy logic controller. Buck converter is required in the PLTS system if the required unidirectional voltage is smaller than the output voltage of the solar cell. Buck converter used to convert 24 Volt dc voltage to 12 Volt dc with 60 watt capability. While fuzzy logic controller is used to improve buck converter performance based on pulse generation technique for switching. The application of fuzzy logic method is expected to improve the performance of the system by maintaining the stability of buck converter output voltage of 12 volts and reduce the output ripple value. Atmega8535 microcontroller is used to generate PWM pulses for switching on power circuits. The results obtained from the test using a 100 Ohm 5 Watt load obtained the buck converter output voltage of 12.4 Volt.

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