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.

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.

scholarly journals Implementation of Fuzzy Logic Control System on Rotary Car Parking System Prototype

2018 ◽  
Vol 12 (2) ◽  
pp. 706
Author(s):  
Nanang Ismail ◽  
Iim Nursalim ◽  
Hendri Maja Saputra ◽  
Teddy Surya Gunawan
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Fuzzy Control ◽  
Rise Time ◽  
Settling Time ◽  
Inference Model ◽  
Time Value ◽  
Parking System ◽  
State Error

Rotary car parking system (RCPS) is one of the effective parking models used in the metropolitan area because the mechanical parking system is designed vertically to conserve the land usage. This paper discussed the implementation of fuzzy logic with the Sugeno Inference Model on the RCPS miniature control system. The research started with kinematics analysis and a mathematical model was derived to determine the slot position and optimal power requirements for each condition. Furthermore, the Fuzzy Inference model used was the Sugeno Model, taking into account two variables: distance and angle. These two variables were selected because in the designed miniature RCPS there will be rotational changes of rotation and rotation in turn. Variable distance was divided into four clusters, such as Zero, Near, Medium and Far. While the angle variables were divided into four clusters as well, such as Zero, Small, Medium, and Big. The test results on a miniature RCPS consisting of six parking slots showed that fuzzy based control provided better results when compared to conventional systems. Step response on the control system without fuzzy control showed the rise time value of 0.58 seconds, peak time of 0.85 seconds, settling time of 0.89, percentage overshoot of 0.20%, and steady state error of 4.14%. While the fuzzy control system provided the rise time value of 0.54 seconds, settling time of 0.83 seconds, steady state error of 2.32%, with no overshoot.

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.

Desain dan Komparasi Kontrol Kecepatan Motor DC

2020 ◽  
Vol 7 (2) ◽  
pp. 127-134
Author(s):  
Safah Tasya Aprilyani ◽  
Irianto Irianto ◽  
Epyk Sunarno
Keyword(s):  
Steady State ◽  
Rise Time ◽  
Settling Time ◽  
Proportional Integral

Penggunaan kontrol sangat diperlukan dalam pengaturan kecepatan motor DC. Dalam pengaturan kecepatan motor DC, salah satu jenis kontrol yang digunakan adalah kontrol Proportional Integral (PI). Untuk 4 jenis metode pada kontrol PI yang digunakan adalah metode Ziegler Nichole, Chien Servo 1, Chien Regulator 1 dan perhitungan secara analitik yang telah diperoleh dari data yang sudah ada.  Namun kontrol dengan PI 4 metode yang digunakan  sebagai pembanding memiliki waktu respon kecepatan saat stabil cenderung lambat baik dari nilai settling time, rise time dan steady state. Maka dari itu dilakukan komparasi antara 4 metode kontrol PI dengan penggunaan kontrol fuzzy. Dalam membandingkan antara 4 metode kontrol PI dan kontrol fuzzy terdapat beberapa parameter sebagai perbandingan yaitu maximum overshoot, steady state, rise time dan settling time. Hasil dari perbandingan tersebut adalah kontrol fuzzy dapat menghasilkan performa lebih baik jika dibandingkan dengan 4 metode pada kontrol PI. Kontrol fuzzy memiliki nilai rise time sebesar 0,015 detik, nilai settling time sebesar 0,025 detik dengan kecepatan sebesar 2900 rpm serta error steady state sebesar 3,33% tanpa adanya overshoot dan osilasi.

Tuning pi untuk stabilitas kecepatan putaran motor induksi 1 fasa pada mesin penyaring bubur kedelai

JURNAL ELTEK ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 48
Author(s):  
Achmad Afandi ◽  
Mila Fauziyah Fauziyah ◽  
Denda Dewatama
Keyword(s):  
Steady State ◽  
Conventional Method ◽  
Rise Time ◽  
Settling Time ◽  
Screening Process ◽  
Pi Method ◽  
Filter Tube ◽  
Manual Methods ◽  
No Innovation

Perusahaan tahu di Indonesia pada umumnya, masih menggunakan cara manual, dalam pembuatannya khususnya pada proses penyaringan bubur kedelai yang membutuhkan waktu yang, lama. Hal tersebut didasarkan pada belum ditemukannya mesin pemeras bubur kedelai. Dewasa ini telah ditemukan inovasi mesin, pemeras bubur kedelai yang bisa meningkatkan kuantitas dan, kualitas produksi dibanding dengan cara manual. Penerapan, teknologinya adalah bubur kedelai diletakkan pada tabung, penyaring kemudian tabung tersebut diputar menggunakan motor, yang dihubungkan melalui fanbelt dan pulley. Ketika motor, diputar, tabung akan ikut berputar sehingga menimbulkan gerak, sentrifugal dimana air kedelai akan terpisah dari ampas. Kecepatan putar motor yang dikontrol adalah 750 rpm dengan, nilai Kp 0,108 , Ki 0,83 sehingga mendapatkan air sari kedelai, sebesar 1,3 liter dengan perbandingan 1 kg kedelai : 1 liter air. Parameter dari penerapan metode PI ini meliputi rise time 4 detik, settling time 4,5 detik, overshoot 0 dan error steady state 2,4%. Dengan penerapan metode PI maka hasil perasan kedelai dari, peyaringan menjadi semakin banyak dan waktu yang dibutuhkan 4 menit lebih singkat dibandingkan dengan cara konvensional.   Tofu companies in Indonesia generally still use manual methods in their manufacture, especially in the soybean slurry screening process which certainly has many disadvantages such as extortion time needed. This was based on the fact that there was no innovation in the soybean pulp squeezer. Currently, it has been found that innovations of soybean slurry machines can increase the quantity and quality of production compared to manual methods. The application of the technology is soybean slurry placed on the filter tube then the tube is rotated using a motor connected with fanbelt and pulley. When the motor is rotated, the tube will rotate, causing centrifugal motion where the soybean water will separate from the pulp. The speed of the motor controlled in 750 rpm with the Kp 0,108, Ki 0,83, to get soybean essence up to 1,3 liter within comparison 1 kg soybean : 1 liter water. The parameter PI method including rise time 4 second, settling time 4,5 second, overshoot 0 and error steady state 2,4%. By applying PI method, the result of filtering is 4 minute faster comparing with conventional 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.

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