Determination of the temperature in the cutting zone while processing machine plastic using fuzzy-logic controller (FLC)

Batteries have an important role in the development of energy needs. A good battery performance, will support the device it supports. The amount of battery that can be stored is limited, the battery will experience a charge and discharge cycle. One aspect of battery management is how the temperature in the battery is always controlled. This will place the battery always in the ideal temperature so that it can work optimally. The stages in software design consist of designing a fuzzy logic controller that uses Arduino software. To display the results of reading the system, the Serial Monitor is used in the Arduino software. From the data, the response from the control used will be observed. The system is controlled using the Fuzzy Logic Controller method. In this experiment, the input used is the LM35 temperature sensor. This value will affect the speed of the DC motor which in this experiment is regulated through the determination of the PWM value. There are 3 PWM values, namely 100 for slow motor speed, 200 for medium motor speed and 255 for fast motor speed.

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ENHANCING THE PERFORMANCE OF THE WALL-FOLLOWING ROBOT BASED ON FLC-GA

SINERGI ◽

10.22441/sinergi.2020.2.008 ◽

2020 ◽

Vol 24 (2) ◽

pp. 141

Author(s):

Heru Suwoyo ◽

Yingzhong Tian ◽

Muhammad Hafizd Ibnu Hajar

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Membership Function ◽

Fuzzy Logic Controller ◽

Optimization Method ◽

Small Error ◽

Robot Accuracy ◽

Comparative Results ◽

Wall Following

Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.

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Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

ACMIT Proceedings ◽

10.33555/acmit.v3i1.43 ◽

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.

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A Self Learning Fuzzy Logic Controller for Ship Steering System

Iraqi Journal for Electrical And Electronic Engineering ◽

10.33762/eeej.2012.79716 ◽

2012 ◽

Vol 8 (1) ◽

pp. 25-34

Author(s):

Ammar A. Aldair

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Steering System ◽

Ship Steering ◽

Self Learning

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STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽

10.33795/eltek.v16i2.104 ◽

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.

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Performance Comparison of MANET, VANET and FANET

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i5.692 ◽

2018 ◽

Vol 8 (5) ◽

pp. 132

Author(s):

Aarti Sahu ◽

Laxmi Shrivastava

Keyword(s):

Fuzzy Logic ◽

Wireless Network ◽

Packet Loss ◽

Ad Hoc Network ◽

Fuzzy Logic Controller ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Performance Comparison ◽

Fuzzy Rules ◽

Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

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