scholarly journals PI Fuzzy Controller of Synchronous Boost Converter for Drug Storage Thermoelectric Cooler

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Alvin Noer Ramadhan ◽  
Novie Ayub Windarko ◽  
Irianto Irianto
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Pi Controller ◽  
Boost Converter ◽  
Settling Time ◽  
Voltage Regulator ◽  
Set Point ◽  
Drug Storage

Medicines should be stored in a room at a suitable temperature if the inappropriate affect the quality of the drug. Therefore we need a control that can control the temperature in the room so that it is constant in accordance with the rules for room temperature in drug storage, which is 25 degrees Celsius. The following paperwork presents a simulation controller between PI controller and PI-Fuzzy logic controller in adjusting the voltage to match the set of point. Where the fuzzy logic controller automatically searches for the Kp value so that the voltage output of the converter match the desired set of point. Then the converter used is synchronoust boost converter as voltage regulator and peltier as a DC load which functions as a cooler. in this research, the system using  PI controller was able to adjust the voltage to match the set point with Kp is 0.14089 and Ki is 124.6738 then settling time is 0.016 s. While the system using PI-Fuzzy logic controller,it was able to adjust the voltage to match the set point with Kp is 0.08112 and Ki is 125.6738 then settling time is 0.014 s.

Response Based Comparative Analysis of Two Inverter Fed Six Phase PMSM Drive by Using PI and Fuzzy Logic Controller

2016 ◽  
Vol 6 (6) ◽  
pp. 2643 ◽  
Author(s):  
Anurag Singh Tomer ◽  
Saty Prakash Dubey
Keyword(s):  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Fuzzy Logic Controller ◽  
Permanent Magnet Synchronous Motor ◽  
Sudden Change ◽  
Pi Controller ◽  
Settling Time ◽  
Steady State Current ◽  
Speed Level ◽  
Starting Torque

<p>This Paper gives a complete modeling and simulation of a two inverter fed six phase permanent magnet synchronous motor drive system, Then response based comparative analysis is done on starting torque ,settling time, Steady state current at various speed levels and torque levels by changing  proportional- integral (PI) controller to  Fuzzy logic controller. The PI controller has some disadvantages like, more settling time, sluggish response due to sudden change in load torque etc. So an intelligent controller, based on fuzzy logic is introduced which replaces the PI-controller and its drawbacks. The performance of both the controller has been investigated and studied by comparing the different plots obtained by setting various speed level both incremented and decremented speed  , at different load conditions like No-load, fix load and dynamic load through Matlab/Simulink environment. Finally it is concluded from the result that fuzzy logic based controller is robust, reliable gives quick response with high starting torque and more effective than the conventional PI controller. It is also observed that both the proposed model can also run above rated speed significantally.</p>

Bioreactor Control Using Fuzzy Logic Controllers

2014 ◽  
Vol 573 ◽  
pp. 291-296 ◽  
Author(s):  
N. Arulmozhi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Pi Controller ◽  
Superior Performance ◽  
Control Problems ◽  
Parameter Uncertainties ◽  
Control Techniques ◽  
Optimal Productivity ◽  
Input Multiplicities

Bioreactors are characterized by high nonlinearities and are often subjected to parameter uncertainties and disturbances. The control of such processes is often difficult to achieve with traditional linear control techniques. In the present work, a Fuzzy logic controller is designed in two versions to a Bioreactor which exhibits input multiplicities in dilution rate on productivity. Fuzzy controller and Fuzzy tuned PI controller is designed to translate the information obtained from the operator’s experiences for designing an automatic control system The Performance of proposed Fuzzy logic controller versions and conventional PI controller have been analyzed and evaluated. The two Fuzzy controller versions provide stable and faster responses than conventional PI controller. Thus, Fuzzy control is found to overcome the control problems of PI controller due to the input multiplicities near optimal productivity. It is interesting to note that the present fuzzy logic controller is giving superior performance. The process is tested with the MATLAB/SIMULINK and Fuzzy Logic Toolbox. The simulation results were presented which illustrate the validity of the method.

scholarly journals Response Based Comparative Analysis of Two Inverter Fed Six Phase PMSM Drive by Using PI and Fuzzy Logic Controller

2016 ◽  
Vol 6 (6) ◽  
pp. 2643
Author(s):  
Anurag Singh Tomer ◽  
Saty Prakash Dubey
Keyword(s):  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Fuzzy Logic Controller ◽  
Permanent Magnet Synchronous Motor ◽  
Sudden Change ◽  
Pi Controller ◽  
Settling Time ◽  
Steady State Current ◽  
Speed Level ◽  
Starting Torque

<p>This Paper gives a complete modeling and simulation of a two inverter fed six phase permanent magnet synchronous motor drive system, Then response based comparative analysis is done on starting torque ,settling time, Steady state current at various speed levels and torque levels by changing  proportional- integral (PI) controller to  Fuzzy logic controller. The PI controller has some disadvantages like, more settling time, sluggish response due to sudden change in load torque etc. So an intelligent controller, based on fuzzy logic is introduced which replaces the PI-controller and its drawbacks. The performance of both the controller has been investigated and studied by comparing the different plots obtained by setting various speed level both incremented and decremented speed  , at different load conditions like No-load, fix load and dynamic load through Matlab/Simulink environment. Finally it is concluded from the result that fuzzy logic based controller is robust, reliable gives quick response with high starting torque and more effective than the conventional PI controller. It is also observed that both the proposed model can also run above rated speed significantally.</p>

scholarly journals ANALISA PENGENDALI HYBRID SLIDING MODE CONTROL DAN FUZZY LOGIC CONTROLLER DALAM PENURUNAN ERROR STEADY STATE UNTUK MENGENDALIKAN CONCENTRATION PADA ISOTHERMAL CONTINUOUS STIRRED TANK REACTOR (CSTR)

2019 ◽  
Vol 6 (1) ◽  
pp. 32-39
Author(s):  
Ahmad Faizal ◽  
Dian Mursyitah ◽  
Ewi Ismaredah
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Sliding Mode Control ◽  
Fuzzy Logic Controller ◽  
Sliding Mode ◽  
Settling Time ◽  
Continuous Stirred Tank Reactor ◽  
Set Point ◽  
Mode Control ◽  
Continuous Stirred Tank

Sistem di industri sering terjadi kesalahan dalam mencapai kinerja atau performansi yang diinginkan. Salah satunya pada sistem isothermal CSTR dimana sistem ini belum mampu bekerja sesuai set point yang diinginkan 1 g.mol/litter, untuk mencapai set point maka digunakan pengendali Sliding Mode Control yang di Hybrid dengan Fuzzy Logic Controller yang diidentifikasi dengan metode FOPDT untuk menurunkan nilai error steady state. hybrid sliding mode control dan fuzzy logic controller telah mencapai nilai set point yang diinginkan yaitu 1 g.mol/litter  dengan waktu tunak/settling time 0.7098 detik, sementara pada pengendali sliding mode control mengalami error steady state sebesar 0.0004 g.mol/litter dengan waktu tunak/settling time 0.7275 detik

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 Evaluasi Performa Fuzzy Logic Controller untuk mengatur kecepatan Motor DC Penguatan Terpisah

2020 ◽  
Vol 12 (2) ◽  
pp. 100-110
Author(s):  
Muhammad Aditya Ardiansyah ◽  
Renny Rakhmawati ◽  
Hendik Eko Hadi Suharyanto ◽  
Era Purwanto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Duty Cycle ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Boost Converter ◽  
Voltage Source ◽  
Single Output ◽  
Multiple Input ◽  
State Error

Beragamnya metode yang ditawarkan oleh fuzzy logic kontroller membuat sebagaian orang meneliti mengenai perbedaan metode inferensi yang digunakan oleh fuzzy logic controller. Sejauh ini terdapat tiga metode fuzzy logic kontroller yang telah dikembangkan yaitu Mamdani, Sugono dan Sukamoto. Pada jurnal ini penggunaan fuzzy logic kontroller akan dievaluasi dengan menggunakan motor dc penguat terpisah sebagai beban untuk melakukan pengaturan kecepatan motor dc. Pada paper ini tujuan utamanya adalah dapat mengendalikan kecepatan dari motor DC Penguatan Terpisah dengan mengatur tegangan jangkar dari motor tersebut. DC motor merupakan salah satu jenis motor memiliki banyak aplikasi dan memiliki kemudahan untuk mengatur kecepatan pada motor tersebut. Logika fuzzy yang digunakan pada studi ini adalah inferensi sugeno dimana dengan konfigurasi Multiple Input Single Output (MiSo). Dimana input berupa error dan perubahan error dan output berupa duty cycle dikarenakan yang dikendalikan oleh logika fuzzy adalah Boost Converter selaku controlled voltage source. Target pada jurnal ini adalah dari kecilnya nilai steady – state error dan minimnya osilasi sehingga mampu membuat sistem lebih stabil. Pada studi ini, Hasil pengujian dilakukan dengan menggunakan Simulink by Matlab dengan Hasil pengujian berupa error rata rata sebesar 5.36%.

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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