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 FPGA Implementation of a Robust MPPT of a Photovoltaic System Using a Fuzzy Logic Controller Based on Incremental and Conductance Algorithm

2019 ◽  
Vol 9 (4) ◽  
pp. 4322-4328 ◽  
Author(s):  
M. Y. Allani ◽  
D. Mezghani ◽  
F. Tadeo ◽  
A. Mami
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Buck Converter ◽  
Photovoltaic System ◽  
Frequency Noise ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Comparison Results ◽  
Power Point

Climate dependence requires robust control of the photovoltaic system. The current paper is divided in two main sections: the first part is dedicated to compare and evaluate the behaviors of three different maximum power point tracking (MPPT) techniques applied to photovoltaic energy systems, which are: incremental and conductance (IC), perturb and observe (P&O) and fuzzy logic controller (FLC) based on incremental and conductance. A model of a photovoltaic generator and DC/DC buck converter with different MPPT techniques is simulated and compared using Matlab/Simulink software. The comparison results show that the fuzzy controller is more effective in terms of response time, power loss and disturbances around the operating point. IC and P&O methods are effective but sensitive to high-frequency noise, less stable and present more oscillations around the PPM. In the second section, the FPGA platform is used to implement the proposed control. The FLC architecture is implemented on an FPGA Spartan 3E using the ISE Design Suite software. Simulation results showed the effectiveness of the proposed fuzzy logic controller.

Experimental Investigations with Fuzzy Controller for PV Fed DC Motor Incorporating SEPIC Converter as Efficient Power Interface

2014 ◽  
Vol 550 ◽  
pp. 110-125
Author(s):  
R.L. Josephine ◽  
S. Suja
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Experimental Investigations ◽  
Pv Array ◽  
Simulink Model ◽  
In Series ◽  
Circuit Components ◽  
Efficient Power

The design and implementation of solar energy fed power electronic interface using SEPIC converter incorporating fuzzy logic controller for a DC motor has been attempted. The proposed scheme consists of a Photovoltaic (PV) array, a SEPIC DC-DC converter, a PIC microcontroller and a DC motor. The SEPIC converter has been fabricated using IGBT and associated circuit components. A PIC microcontroller has been programmed to automatically vary the duty cycle of the SEPIC converter with fuzzy logic controller depending upon the required speed of the motor. The Simulink model of the proposed scheme has been built using MATLAB/PSB. To test the satisfactory performance of the program written for PIC microcontroller, the program is loaded into the PROTEUS VSM simulator and the waveform of the gate pulses obtained from the simulator using MPLAB coding is studied. After confirming the satisfactory generation of the gate pulses by the simulator, the program is loaded into PIC microcontroller using PIC start plus and gate pulses generated is fed to the DC-DC converter for firing the IGBT. Experiments have been carried out on a 230V, 4.5A, 0.75 kW, 1500 rpm separately excited DC motor and the results are furnished for different load conditions. Various reference speeds have been set and the system automatically adjusts the actual speed of the DC motor close to the set speed. The PV array used in the system consists of the panels connected in series, each panel being rated for 18V and 5A. The comparison of experimental and simulation results show very close agreement between the two thus validating the proposed scheme.

Fuzzy Logic Control of Photovoltaic Lead-Acid Charging System

2011 ◽  
Vol 219-220 ◽  
pp. 941-944 ◽  
Author(s):  
Kao Feng Yarn ◽  
King Kung Wu ◽  
Long Yeu Chung
Keyword(s):  
Fuzzy Logic ◽  
Solar Cell ◽  
Digital Signal Processor ◽  
Fuzzy Logic Control ◽  
Control Method ◽  
Low Voltage ◽  
Buck Converter ◽  
Charging System ◽  
Logic Control ◽  
Lead Acid

A new photovoltaic solar cell charging system with fuzzy logic control is proposed and designed. This kind of solar energy storage system is composed of a solar cell, a charger, batteries, a buck converter and a digital signal processor. It mainly applies a low-voltage level translator to control the pulse charging current of a lead-acid battery and also combines the fuzzy control method to improve the charging efficiency, suppress the abnormal temperature rise in the battery, lengthen the battery life-time, reduce the additional waste, etc..Experimental and simulated results are shown to demonstrate and compare the validity of the system and verify the effectiveness with each other.

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.

Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

2011 ◽  
Vol 403-408 ◽  
pp. 5068-5075
Author(s):  
Fatma Zada ◽  
Shawket K. Guirguis ◽  
Walied M. Sead
Keyword(s):  
Fuzzy Logic ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
System Response ◽  
Robot Arm ◽  
Neural Controller ◽  
Hybrid Controller ◽  
Neural Fuzzy ◽  
Transient And Steady State

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

2019 ◽  
Vol 20 (1) ◽  
pp. 51-62
Author(s):  
Rajmeet Singh ◽  
Tarun Kumar Bera
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Bond Graph ◽  
Membership Functions ◽  
Current Position ◽  
Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

scholarly journals Implementation of Fuzzy Logic Controller in Solar PV Array Based AC Drives

2019 ◽  
Vol 8 (2S7) ◽  
pp. 423-428
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Solar Pv ◽  
Ac Drives ◽  
Pv Array ◽  
Ac Drive ◽  
Harmonic Content

This paper shows the real implementation of fuzzy logic controller in an AC drive system (Induction motor Drive) under Solar PV array-based system. The switching of boost converter is controlled with help of fuzzy logic by taking inputs from solar PV array while For Inverter switching is done by using fuzzy logic controller by taking inputs from induction motor drive. Initially, 20 kW solar PV array is designed for feeding the 10Kw Induction motor drive with the help MATLAB/SIMULINK. The complete system gives reliable, smooth, efficient, lesser harmonic content level in the output.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

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