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. 

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.

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 Objective functions modification of GA optimized PID controller for brushed DC motor

2020 ◽  
Vol 10 (3) ◽  
pp. 2426
Author(s):  
A.A.M. Zahir ◽  
Syed Sahal Nazli Alhady ◽  
A.A.A Wahab ◽  
M.F. Ahmad
Keyword(s):  
Steady State ◽  
Objective Function ◽  
Rise Time ◽  
Pid Controller ◽  
Optimization Method ◽  
Settling Time ◽  
Performance Parameter ◽  
Objective Functions ◽  
Performance Requirement ◽  
State Error

PID Optimization by Genetic Algorithm or any intelligent optimization method is widely being used recently. The main issue is to select a suitable objective function based on error criteria. Original error criteria that is widely being used such as ITAE, ISE, ITSE and IAE is insufficient in enhancing some of the performance parameter. Parameter such as settling time, rise time, percentage of overshoot, and steady state error is included in the objective function. Weightage is added into these parameters based on users’ performance requirement. Based on the results, modified error criteria show improvement in all performance parameter after being modified. All of the error criteria produce 0% overshoot, 29.51%-39.44% shorter rise time, 21.11%-42.98% better settling time, 10% to 53.76% reduction in steady state error. The performance of modified objective function in minimizing the error signal is reduced. It can be concluded that modification of objective function by adding performance parameter into consideration could improve the performance of rise time, settling time, overshoot percentage, and steady state error

Development of Servo Drive Positioning Control System Using PID Controller and Fuzzy Logic Controller

2021 ◽  
Vol 26 (6) ◽  
pp. 583-588
Author(s):  
Zaw Myo Naing ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Settling Time ◽  
Fuzzy Pid ◽  
Servo Drive ◽  
Positioning Control ◽  
Fuzzy Pid Controller

Servo drives are one of the most widely utilized devices in various mechanical systems and industrial applications to provide precise position control. The study of servo driver produc-tiveness and performance index is the important task. In this work, PID controller and fuzzy log-ic controller (FLC) were developed to control the position of a DC servo drive. The MATLAB Simulink program was investigated and implemented to calculate the values of servo drive pa-rameters, and a scheme for simulating the operation of a servo drive using different controllers was presented. A mathematical model of a DC servo drive for a positioning control system has been proposed. The control characteristics of the PID controller, fuzzy logic controller and fuzzy PID controller are compared. The simulation results have shown that the PID controller allows for an overshoot of about 1 % with a settling time of about 4 sec. The use of the fuzzy PID con-troller reduces the maximum overshoot to 1 % and decreases the settling time to 2 sec. As a re-sult, the fuzzy PID controller allows for better performance and efficiency compared to other controllers.

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 Performance Evaluation of Balancing Bicopter using P, PI, and PID Controller

2019 ◽  
Vol 11 (2) ◽  
pp. 44-49
Author(s):  
Esa Apriaskar ◽  
Fahmizal Fahmizal ◽  
Nur Azis Salim ◽  
Dhidik Prastiyanto
Keyword(s):  
Performance Evaluation ◽  
Steady State ◽  
Pid Controller ◽  
Performance Test ◽  
Pulse Width Modulation ◽  
Pi Controller ◽  
Settling Time ◽  
Proportional Integral ◽  
Rising Time ◽  
State Error

Due to potential features of unmanned aerial vehicles for society, the development of bicopter has started to increase. This paper contributes to the development by presenting a performance evaluation of balancing bicopter control in roll attitude. It aims to determine the best controller structure for the balancing bicopter. The controller types evaluated are based on Ziegler-Nichols tuning method; they are proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers. Root locus plot of the closed-loop balancing bicopter system is used to decide the tuning approach. This work considers a difference in pulse-width-modulation (PWM) signal between the left and right rotors as the signal control and bicopter angle in roll movement as the output. Parameters tuned by the method are Kp, Ti, and Td which is based on the ideal PID structure. The performance test utilizes rising time, settling time, maximum overshoot, and steady-state error to determine the most preferred controller. The result shows that PI-controller has the best performance among the other candidates, especially in maximum overshoot and settling time. It reaches 8.34 seconds in settling time and 3.71% in maximum overshoot. Despite not being the best in rising time and resembling PID-controller performances in steady-state error criteria, PI-controller remains the most preferred structure considering the closeness of the response to the desired value.

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