scholarly journals ANALYSIS OF PID CONTROLLER USE ON AUXILIARY WINDING INDUCTION MOTOR WITH FUZZY LOGIC METHOD

2021 ◽  
Vol 9 (8) ◽  
pp. 320-334
Author(s):  
Hartono ◽  
Karim Amarullah ◽  
Alief Maulana
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
No Value ◽  
Rise Time ◽  
Pid Controller ◽  
Rotor Speed ◽  
Main Current ◽  
Fuzzy Logic Method ◽  
Main Voltage ◽  
Logic Method

Controlling the control of an induction motor with a stable and fast speed is very much needed in the industrial. To get a constant speed and be able to improve the performance of the induction motor, a PID controller circuit is needed. Fuzzy logic method is known to work with a fast response and enough good performance. The value of the motor speed formed with PID control and fuzzy logic, the rotor speed with fuzzy is the same as that without using fuzzy of 1785 rpm with the same overshoot and fuzzy rise time value of 346,605 ms and the value of rise time without fuzzy of 346,111 ms. The results obtained in the Main current without fuzzy of 2.2 A, main current with fuzzy of 2.2 A, aux. Current without fuzzy is 1.6 A, aux. Current with fuzzy 1.9 A, load and elctromag torque without fuzzy no value, load and elctromag torque with fuzzy no value, main voltage without fuzzy 154 V, main voltage with fuzzy 154 V, aux. Voltage without fuzzy 180 V, aux. Voltage with fuzzy is 184 V, rotor speed without fuzzy is 1785 rpm, rotor speed with fuzzy is 1785 rpm.

scholarly journals Rancang Bangun Penstabil Drone S2GA Berbasis Metode Fuzzy Logic Menggunakan Arduino

2020 ◽  
Vol 6 (2) ◽  
pp. 104-112
Author(s):  
Wahyu Pambudi ◽  
Yudhi Darmawan ◽  
Priska Choirina
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Stability Control ◽  
Settling Time ◽  
Advanced Technology ◽  
Original Position ◽  
Design System ◽  
Fuzzy Logic Method ◽  
Logic Method

UAV merupakan wahana teknologi canggih yang sering digunakan di bidang militer untuk misi pengintaian. UAV terdiri dari beragam jenis, salah satunya yaitu quadcopter. Quadcopter yang digunakan dalam misi militer biasanya mempunyai masalah ketidakstabilan ketika quadcopter tersebut terbang membawa senjata. Oleh karena itu, maka diperlukan sebuah sistem untuk mengatur kestabilan dari percepatan motor quadcopter. Pada paper ini dipaparkan sebuah desain system dari stabilizer drone dengan metode logika fuzzy menggunakan 3 derajat. Penelitian ini bertujuan untuk mengkonfigurasikan kontrol kestabilan quadcopter yang optimal setelah diterapkan metode fuzzy logic inferensi Tsukamoto. Input dari system ini adalah percepatan dan perubahan percepatan. Sedangkan output yang dihasilkan berupa kecepatan motor. Untuk mengetahui error dilakukan pengujian ketepatan posisi 5 kali pada ketinggian 1-3 meter. Sedangkan untuk mendapatkan waktu quadcopter untuk kembali ke posisi semula dapat menggunakan stopwatch. Penelitian ini bertujuan untuk mengkonfigurasikan kontrol kestabilan quadcopter yang optimal setelah diterapkan metode fuzzy logic inferensi Tsukamoto. Hasil penelitian dengan logika fuzzy untuk kestabilan menunjukan nilai rise time sebesar 0,7 detik, settling time 2,55 detik, overshoot sebesar 15 % ketika menerima gangguan sebesar 45cm, dan nilai steady-state 69,55 cm dengan simpangan baku sebesar ± 1,775 cm. Hasil tersebut memberikan akurasi dalam menentukan kestabilan yang lebih baik pada quadcopter. UAV is one of the advanced technology that used in the military for reconnaissance missions. UAV consists of various types, one of them is a quadcopter. Since the quadcopter in military missions has an instability problem when they fly with a weapon, they needed to stabilize the acceleration of a quadcopter motor. This paper presents a design system of drone stabilizer using fuzzy logic method based on 3 degrees of freedom to improve stability. Fuzzy logic that used to configure optimal quadcopter stability control is Tsukamoto's inference fuzzy logic method. The input of this system are acceleration and acceleration change. While, the output of this system is the speed of motor. We did 5 times experiment to find out the accuracy of this system at an altitude of 1-3 meters. Furthermore, to get the quadcopter time from return to its original position we used a stopwatch. Based on the experiments, we obtained a rise time value of 0.7 seconds, settling time of 2.55 seconds, overshoot of 15% when receiving interference of 45cm, and a steady-state value of 69.55 cm with a standard deviation of ± 1.775 cm. These result show that fuzzy logic provide a better accuracy in determining stability on quadcopter.

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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