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.

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. 

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 Evaluasi Performa Fuzzy Logic Controller untuk mengatur kecepatan Motor DC Penguatan Terpisah

2020 ◽  
Vol 12 (2) ◽  
pp. 100-110
Author(s):  
Muhammad Aditya Ardiansyah ◽  
Renny Rakhmawati ◽  
Hendik Eko Hadi Suharyanto ◽  
Era Purwanto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Duty Cycle ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Boost Converter ◽  
Voltage Source ◽  
Single Output ◽  
Multiple Input ◽  
State Error

Beragamnya metode yang ditawarkan oleh fuzzy logic kontroller membuat sebagaian orang meneliti mengenai perbedaan metode inferensi yang digunakan oleh fuzzy logic controller. Sejauh ini terdapat tiga metode fuzzy logic kontroller yang telah dikembangkan yaitu Mamdani, Sugono dan Sukamoto. Pada jurnal ini penggunaan fuzzy logic kontroller akan dievaluasi dengan menggunakan motor dc penguat terpisah sebagai beban untuk melakukan pengaturan kecepatan motor dc. Pada paper ini tujuan utamanya adalah dapat mengendalikan kecepatan dari motor DC Penguatan Terpisah dengan mengatur tegangan jangkar dari motor tersebut. DC motor merupakan salah satu jenis motor memiliki banyak aplikasi dan memiliki kemudahan untuk mengatur kecepatan pada motor tersebut. Logika fuzzy yang digunakan pada studi ini adalah inferensi sugeno dimana dengan konfigurasi Multiple Input Single Output (MiSo). Dimana input berupa error dan perubahan error dan output berupa duty cycle dikarenakan yang dikendalikan oleh logika fuzzy adalah Boost Converter selaku controlled voltage source. Target pada jurnal ini adalah dari kecilnya nilai steady – state error dan minimnya osilasi sehingga mampu membuat sistem lebih stabil. Pada studi ini, Hasil pengujian dilakukan dengan menggunakan Simulink by Matlab dengan Hasil pengujian berupa error rata rata sebesar 5.36%.

scholarly journals Rancang Bangun Farming Box Dengan Pengaturan Suhu Menggunakan Fuzzy Logic Controller

ELKHA ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 92
Author(s):  
Riza Agung Firmansyah ◽  
Dani Junianto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Air Temperature ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Data Feedback ◽  
Shipping Containers ◽  
Fan Speed ◽  
State Error

Implementation of control systems has been carried out in many fields of science. One of it applications is in the agriculture fields. In this research we implemented a control system on farming in a box. Farming in a box is a system that uses old shipping containers for the purpose of growing plants in any environment. Inside shipping containers is fully assembled hydroponic pipe with air temperature control. In this research was built a little farming box from acryclic to imitate a shipping container. Main focus of this research is design an air temperature control using fuzzy logic controller. Fuzzy logic controller was choosen because many existing farming box use on off controller. In some application, fuzzy logic controller has better performance than on off controller. Farming box temperature is controlled by blowing cool air using an electric fan. In this case, cool air is produced by cold side of peltier. Electric fan speed is controlled by pulse width modulation signal (PWM) that generated from microcontroller. Air temperature data feedback is obtained from DHT 11 sensor that installed in a acrylic box. Sensor is physically connected with microcontroller and Fuzzy logic controller is embedded in microcontroller as an algorithm. Fuzzy logic controller was design with error temperature and error difference as an input, and duty cycle of PWM signal as output. Fuzzy logic controller system performs to reduce the temperature from 31,6 ° C to set poin 28° C in 71 seconds. Steady state error obtained by 1.28% and better than uncontrolled system that obtain steady state error 7,14%.

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 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

Implementasi Kontroller Adaptive Fuzzy Pada Pengaturan Level Coupled Tank

2017 ◽  
Vol 2 (1) ◽  
pp. 101-107
Author(s):  
Mohammad Erik Echsony ◽  
Dirvi Eko Juliando Sudirman
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Steady State ◽  
Fuzzy Logic Control ◽  
Settling Time ◽  
Adaptive Fuzzy ◽  
Logic Control ◽  
Auto Tuning

Permasalahan pada Coupled Tank adalah   munculnya ganguan pada flow yang menyuplai tangki akan membuat respon tidak stabil, sehingga interaksi silang antar masing-masing input dan outputnya.   Fuzzy   Logic   Control   (FLC) memiliki   keunggulan   tersendiri   yaitu   efektif   dalam menghadapi sistem non-linier yang kompleks. Kontrol Artificial Intelligence (AI) berbasis dirancang untuk kombinasi dari PI dan FL. Kontrol Fuzzy PI Auto tuning memiliki kemampuan untuk mempertahankan   nilai respon steady state dari ganguan.Metodologi Fuzzy PI adalah pilihan yang menarik ketika formulasi dalam metode yang diusulkan untuk proses Coupled tank. kontrol Fuzzy PI  memiliki kemampuan untuk mempertahankan nilai respon steady state dari  ganguan.  Pada penelitian  ini  menggunakan  decoupling  pada  proses  interaksi  silang  pada  masing-masing  tanki. Coupled   Tank  pada  sistem  TITO  dapat  mengubah  fungsi  transfer  menjadi  SISO,  sehingga  dapat meminimalkan  pengaruh  berinteraksi.  Cara  merancang    pemodelan  yang  tepat  ialah  dengan memperhatikan  state    yang  ada  pada  plant  tersebut,  sehingga  model  kontrol  yang  diingkinkan  dapat  mengatasi non linearity dari sistem TITO. Metode Fuzzy PI dengan Decoupling di harapkan memiliki  nilai percent overshoot dan settling time lebih baik.

Application of a Fuzzy Logic Controller for Speed Control on a Small-Scale Turbojet Engine

2014 ◽  
Author(s):  
Serdar Üşenmez ◽  
Sinan Ekinci ◽  
Oğuz Uzol ◽  
İlkay Yavrucuk
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Transient Response ◽  
Fuzzy Logic Controller ◽  
Electronic Control Unit ◽  
Small Scale ◽  
Gas Temperature ◽  
Fuel Pump ◽  
Fuel Flow ◽  
Turbojet Engine

Having a small-scale turbojet engine operate at a desired speed with minimum steady state error, while maintaining good transient response is crucial in many applications, such as UAVs, and requires precise control of the fuel flow. In this paper, first the mathematical model of a Small-Scale Turbojet Engine (SSTE) is obtained using system identification tests, and then based on this model, a classical PI controller is designed. Afterwards, to improve on the transient response and steady state performance of this classical controller, a Fuzzy Logic Controller (FLC) is designed. The design process for the FLC employs logical deduction based on knowledge of the engine behavior and iterative tuning in the light of software- and hardware-in-the-loop simulations. The classical and fuzzy logic controllers are both implemented on an in-house, embedded Electronic Control Unit (ECU) running in real time. This ECU is an integrated device carrying a microcontroller based board, a fuel pump, fuel line valves, speed sensor and exhaust gas temperature sensor inputs, and starter motor and glow plug driver outputs. It mainly functions by receiving a speed reference value via its serial communication interface. Based on this reference, a voltage is calculated and applied to the fuel pump in order to regulate the fuel flow into the engine, thereby bringing the engine speed to the desired value. Pre-defined procedures for starting and stopping the engine are also automatically performed by the ECU. Further, it connects to a computer running an in-house comprehensive Graphical User Interface (GUI) software for operating, monitoring, configuration and diagnostics purposes. The designed controllers are used to drive a generic SSTE. Reference inputs consisting of step, ramp and chirp profiles are applied to the controllers. The engine response using both controllers are recorded and inspected. The results show that the FLC exhibits a comparable performance to the classical controller, with possible opportunities to improve this performance.

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