Development and simulation of an agriculture control system using fuzzy logic method and Visual Basic environment

2013 ◽  
Author(s):  
Mohd Azlan Abu ◽  
Mohamad Yusri Yacob
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Visual Basic ◽  
Fuzzy Logic Method ◽  
Logic Method

scholarly journals Penerapan Metode Fuzzy Sugeno Sebagai Sistem Navigasi Robot Menggunakan Mikrokontroler Arduino dan Sensor Ultrasonik

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Husnawati Husnawati
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Good Control ◽  
Ultrasonic Sensor ◽  
Human Needs ◽  
Fuzzy Logic Method ◽  
Arduino Uno ◽  
Work Done ◽  
Robot Path ◽  
Logic Method

<p align="center"><strong><em>Abstract <br /></em></strong></p><p><em>In the era of industrial technology and automation, robots are widely used as a tool that can ease human work, so the robot which has a good control system is needed so that the work done by the robot can be optimized. The development of control systems in robots is currently quite rapid along with increasingly diverse human needs. Several methods are applied to the robot control system automatically. Fuzzy logic is a method that is most used in navigating robot motion. In this study will be applied Sugeno type fuzzy logic method as a robot navigation system which embedded in Arduino Uno microcontroller and ultrasonic sensor is used as distance input value, in each robot path is given 30 cm obstacle so that the PWM value generated by the robot can be measured. The results obtained in this study is the Sugeno fuzzy logic method which embedded in the Arduino Uno microcontroller can control the motion of the robot and avoid the obstacles in the environment well.</em></p><p><strong><em>Keywords</em></strong><em>:</em><em> Control System, Navigation, Fuzzy Logic, Arduino, Ultrasonic Sensor</em></p><p align="center"><em> </em></p><p align="center"><strong><em>Abstrak <br /></em></strong></p><p><em>Pada era teknologi dan otomasi industri saat ini banyak digunakan robot sebagai alat bantu yang dapat meringankan pekerjaan manusia, sehingga dibutuhkan robot yang memiliki sistem pengendalian yang baik agar pekerjaan yang dilakukan oleh robot tersebut dapat lebih optimal. Perkembangan sistem pengendalian pada robot saat ini cukup pesat seiring dengan kebutuhan manusia yang semakin beragam. Beberapa metode banyak diterapkan pada sistem pengendalian robot secara otomatis. Logika fuzzy merupakan metode yang banyak digunakan dalam navigasi gerak robot. Sehingga pada penelitian ini akan diterapkan metode logika fuzzy dengan tipe Sugeno sebagai sistem navigasi robot yang ditanamkan pada mikrokontroler Arduino uno dan digunakan sensor ultrasonik sebagai nilai inputan jarak, pada masing – masing lintasan robot diberikan halangan sejauh 30 cm sehingga nilai pwm yang dihasilkan oleh robot dapat terukur. Hasil yang diperoleh pada penelitian ini adalah metode logika fuzzy Sugeno yang ditanamkan pada mikrokontroler Arduino uno dapat mengendalikan gerak robot dan menghindari halangan yang berada di sekitar lingkungan dengan baik.</em></p><strong><em>Kata kunci </em></strong><em>: Sistem Kendali, Navigasi, Logika Fuzzy, Arduino, Sensor Ultrasonik.</em>

PID plus fuzzy logic method for torque control in traction control system

2012 ◽  
Vol 13 (3) ◽  
pp. 441-450 ◽  
Author(s):  
H. -Z. Li ◽  
L. Li ◽  
L. He ◽  
M. -X. Kang ◽  
J. Song ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Torque Control ◽  
Traction Control ◽  
Traction Control System ◽  
Fuzzy Logic Method ◽  
Logic Method

scholarly journals Using WSR-88D Polarimetric Data to Identify Bird-Contaminated Doppler Velocities

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Jiang ◽  
Qin Xu ◽  
Pengfei Zhang ◽  
Kang Nai ◽  
Liping Liu
Keyword(s):  
Fuzzy Logic ◽  
Real Data ◽  
Radar Data ◽  
Second Step ◽  
Doppler Velocity ◽  
Data Quality Control ◽  
Fuzzy Logic Method ◽  
Automated Technique ◽  
Migrating Birds ◽  
Logic Method

As an important part of Doppler velocity data quality control for radar data assimilation and other quantitative applications, an automated technique is developed to identify and remove contaminated velocities by birds, especially migrating birds. This technique builds upon the existing hydrometeor classification algorithm (HCA) for dual-polarimetric WSR-88D radars developed at the National Severe Storms Laboratory, and it performs two steps. In the first step, the fuzzy-logic method in the HCA is simplified and used to identify biological echoes (mainly from birds and insects). In the second step, another simple fuzzy logic method is developed to detect bird echoes among the biological echoes identified in the first step and thus remove bird-contaminated velocities. The membership functions used by the fuzzy logic method in the second step are extracted from normalized histograms of differential reflectivity and differential phase for birds and insects, respectively, while the normalized histograms are constructed by polarimetric data collected during the 2012 fall migrating season and sorted for bird and insects, respectively. The performance and effectiveness of the technique are demonstrated by real-data examples.

scholarly journals Feature Extraction in Six Blocks to Detect and Recognize English Numbers

2021 ◽  
pp. 3790-3803
Author(s):  
Heba Kh. Abbas ◽  
Haidar J. Mohamad
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Suggested Method ◽  
Support Vector ◽  
Horizontal Line ◽  
Svm Algorithm ◽  
Fuzzy Logic Method ◽  
Logic Method

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

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