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.

Hovering Control of Quadrotor Based on Fuzzy Logic

2017 ◽  
Vol 8 (1) ◽  
pp. 492
Author(s):  
Nia Maharani Raharja ◽  
Eka Firmansyah ◽  
Adha Imam Cahyadi ◽  
Iswanto Iswanto
Keyword(s):  
Fuzzy Logic ◽  
Rise Time ◽  
Control Algorithm ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Altitude Control ◽  
Rotary Wing ◽  
Hovering Control ◽  
Maximum Rise

Quadrotor is one of rotary wing UAV types which is able to perform a hover position. In order to take off, landing, and hover, it needs controllers. Conventional controllers have been widely applied in quadrotor, yet they have drawbacks namely overshoot. This paper presents attitude and altitude control algorithm in order to obtain a response as quadrotor hovered optimally within minimum overshoot, rise time, and settling time. The algorithm used is Fuzzy Logic Controller (FLC) algorithm with Mamdani method. By using the algorithm, the quadrotor is able to hover with minimum overshoot and maximum rise time. The advantage of the algorithm is that it does not require linearization model of the quadrotor.

scholarly journals Meningkatkan kinerja motor induksi menggunakan teknologi fuzzy logic controller berbasis artificial intelligence

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
I Putu Sutawinaya ◽  
◽  
Anak Agung Ngurah Made Narottama ◽  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Steady State ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Peak Time ◽  
State Error

Motor induksi adalah merupakan motor listrik arus bolak balik (AC) yang umum digunakan pada industri-industri karena memiliki beberapa keuntungan, diantaranya relatif murah, kokoh serta handal. Namun kelemahan motor induksi saat terjadi perubahan torsi beban secara mendadak, maka akan terjadi penurunan kinerja (performansi) motor. Hal tersebut akan berpengaruh terhadap kestabilan putaran motor, di mana overshoot maupun undershoot relatif tinggi serta risetime relatif lambat. Untuk mengantispasi hal tersebut dibutuhkan sistem kontrol kecepatan motor induksi yang tentunya dapat meningkatkan kinerja motor induksi tersebut. Dalam penelitian ini dilakukan pengujian terhadap sistem kontrol kecepatan motor induksi menggunakan teknologi Fuzzy Logic Controller (FLC) melalui simulasi perangkat lunak Matlab. Dilakukan pengujian terhadap perubahan kinerja motor induksi melalui pemberian torsi beban serta setpoint yang berubah-ubah. Adapun hasil simulasi menunjukan bahwa performansi motor induksi, seperti undershoot, overshoot dan steady state error relatif kecil serta peak time, risetime dan settling time relatif cepat. Sistem yang dirancang mampu menurunkan arus start rata-rata sekitar 72,7% dan torsi awal rata-rata sekitar 81,8% terhadap kondisi idealnya.

scholarly journals Sistem Kontrol Troli Rotari sebagai Tempat Penitipan Barang Otomatis menggunakan Fuzzy Logic

2020 ◽  
Vol 8 (3) ◽  
pp. 575
Author(s):  
RISNANDA SATRIATAMA ◽  
DENNY DARLIS ◽  
PORMAN PANGARIBUAN
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Pulse Width ◽  
Completion Time ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Settling Time ◽  
Load Test ◽  
Rotary Encoder

ABSTRAKTroli rotari memerlukan sistem kontrol untuk mengatur rak ke posisi yang diinginkan. Penelitian ini berfokus pada sistem kontrol posisi rak menggunakan metode Fuzzy Logic Controller (FLC) dengan beban berbeda dari setiap pengguna. Masukan pada sistem kontrol FLC adalah error dan delta error dari sensor rotary encoder. Keluaran dari FLC adalah Pulse Width Modulation yang digunakan untuk mengontrol kecepatan motor DC. Hasil penelitian dari tiga variasi fungsi keanggotaan keluaran dengan beban pada satu rak, pengujian tanpa beban memiliki settling time antara 3,11 s hingga 3,24 s dan error steady state antara 3 hingga 8 counter. Pengujian dengan beban 250 g memiliki settling time antara 3,92 s hingga 8,80 s dan error steady state antara –5 counter hingga 4 counter. Sedangkan pengujian dengan beban 500 g memiliki settling time antara 4,66 s hingga 7,39 s dan error steady state antara 8 counter hingga 12 counter.Kata kunci: tempat penitipan barang, troli rotari, Fuzzy Logic Controller. ABSTRACTRotary trolley needs control system that used for rack control to the position. The research focused on rack position control system using the Fuzzy Logic Controller (FLC) method with different loads from each user. Inputs to the FLC control system are error and delta error from the rotary encoder sensor. The output of the FLC is Pulse Width Modulation which is used to control the speed of the DC motor. The results from 3 variations of the meeting results, the no-load test had a completion time of between 3.11 s to 3.24 s and steady-state conditions between 3 counters to 8 counters. Testing with a load of 250 g has a completion time of 3.92 s to 8.80 s and steady-state conditions between -5 counters to 4 counters. While testing with a load of 500 g has a settling time of 4.66 s to 7.39 s and steady-state conditions between 8 to 12 counters.Keywords: deposit box, rotary trolley, Fuzzy Logic Controller.

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 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 COMPARATIVE STUDY OF MAMDANI AND 1 ST -ORDER TAKAGI SUGENO KANG TYPE FUZZY LOGIC CONTROLLERS FOR CONTROLLING THE RIGID GANTRY CRANE SYSTEM

2018 ◽  
Vol 40 (2) ◽  
pp. 57
Author(s):  
Edwar Yazid ◽  
Rifa Rahmayanti
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Gantry Crane ◽  
Peak Time ◽  
Fuzzy Logic Controllers ◽  
Simulation Results ◽  
Takagi Sugeno

Controlling the rigid gantry crane system is challenging due to it being an under-actuated system. This paper addresses the challenge by presenting the fuzzy logic controller (FLC) with Mamdani and the 1 st -order Takagi Sugeno Kang (TSK) types presenting it in this comparative study. Both controllers are proposed to control the position of the crane while suppressing the swing of the payload. Simulation results show that the Mamdani type outperforms the 1 st -order Takagi Sugeno Kang (TSK) type in terms of no overshoot, though the earlier controller (Mamdani) has a slower rise time, settling time and peak time than the latter controller (TSK).

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

scholarly journals A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5323 ◽  
Author(s):  
José R. García-Martínez ◽  
Edson E. Cruz-Miguel ◽  
Roberto V. Carrillo-Serrano ◽  
Fortino Mendoza-Mondragón ◽  
Manuel Toledano-Ayala ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Motion Control ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Industrial Applications ◽  
Settling Time ◽  
Self Tuning ◽  
S Curve ◽  
Tuning Strategy

Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.

POINT-TO-POINT (PTP) CONTROL PERFORMANCES OF AN UPPER LIMB ROBOTIC ARM

2016 ◽  
Vol 78 (7) ◽  
Author(s):  
Mariam Md Ghazaly ◽  
Ting Huan Teo ◽  
Vivek A/L Regeev ◽  
Kartikesu A/L Vijayan ◽  
Chong Shin Hong ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Upper Limb ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Robotic Arm ◽  
Point To Point ◽  
Precision Motion ◽  
State Error

The objective of this paper is to design a controller which is able to control the output angle for an upper limb of a robotic arm, for precision motion and high speed response.  The aim is to optimize the best controller for an upper limb robotic arm system for precision motion, in which improper motion will results in injuries/ fatality and loss of production in manufacturing system. In this research, a robotic arm prototype with a 1 degree-of-freedom (DOF) was designed and fabricated, in which the DC geared motor was implemented.  Studies are carried out based on previous research to investigate the suitable type of controller. PID controller and fuzzy logic controller are chosen and compared in terms of their performances such as the steady-state error, settling time, rise time and overshoot. The equipment’s used are Micro-Box 2000/2000C, Cytron DC geared motor, motor driver circuit. Micro-Box module acts as the interface between hardware component and MATLAB R2009a. Open-loop simulations are carried out to obtain the transfer function of the motor and substituted into the system for further simulation analysis. Simulation for the uncompensated system is carried out to observe the close-loop system characteristic without the controller. After that, the close-loop point-to-point (PTP) trajectory control for simulations & experiments are carried out for the compensated systems using PID controller based on the Ziegler-Nichols frequency response method. Analyses are made based on the results obtained and the best type of controller is chosen for achieving precise motion control for the upper limb robotic arm. In this paper, the PID controller shows better performances compared to the Fuzzy Logic controller (FLC) with the steady state error of less than 0.010 and settling time of 0.5s; for the input reference of 150  respectively. 

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.

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