Intelligent Control for Visual Servoing System

2017 ◽  
Vol 6 (1) ◽  
pp. 72
Author(s):  
Dwi Pebrianti ◽  
Ong Ying Peh ◽  
Rosdiyana Samad ◽  
Mahfuzah Mustafa ◽  
N. R.H Abdullah ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Visual Servoing ◽  
Settling Time ◽  
Experimental Result ◽  
Threshold Method ◽  
Logic Control ◽  
Different Types ◽  
Tracking And Recognition

<p>This paper presents intelligent control for visual servoing system. The proposed system consists of a camera placed on a Pan Tilt Unit (PTU) which consists of two different servo motors. Camera and PTU are connected to a personal computer for the image processing and controlling purpose. Color threshold method is used for object tracking and recognition. Two different control methods, PID and Fuzzy Logic Control (FLC) are designed and the performances are compared through simulation. From the simulation result, the settling time of PID controller is 40 times faster than FLC. Additionally, the rise time of PID is about 20 times faster than FLC. However, the overshoot percentage of PID controller is 4 times higher than FLC. High overshoot value is not preferable in a control system, since it will cause the damage to the system. Real implementation of FLC on a home-built visual servoing system is conducted. Two different types of FLC, 9 and 11 rules of FLC are designed and implemented on the system. The experimental result shows that FLC with different total number of rules give different system performance. The settling time of FLC with 11 rules is 2 times faster than FLC with 9 rules. Additionally, the overshoot percentage of FLC with 11 rules is 2 times lower than FLC with 9 rules.</p>

Fuzzy Logic Controller Sebagai Pengatur Kecepatan Motor Shunt

2017 ◽  
Vol 8 (1) ◽  
pp. 11-16
Author(s):  
Machrus Ali ◽  
Budiman ◽  
Yanuangga Gala Hartlambang ◽  
4 Dwi Ajiatmo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Logic Control

Telah banyak penelitian pada motor shunt, karena kumparan penguat medan diparalel terhadap kumparan armatur. Motor DC shunt tidak terlalu membutuhkan banyak ruangan karena diameter kawat kecil, tetapi daya keluaran yang dihasilkan kecil karena arus penguatnya kecil. Metode Fuzzy Logic Control (FLC) telah banyak digunakan untuk optimasi suatu system. Penelitian ini membandingkan antara desain tanpa controller, dengan PID controller, dan FLC controller. Dari ketiga desain, menunjukkan bahwa desain control Fuzzy Logic Controller terbaik dari ketiga desain dengan besar putaran 300.0 rpm dengan settling time 1.702 detik dan besar Arus Rotor Motor Shunt (A) sebesar 1.9598 A, dengan setling time 1.323 detik. Penelitian ini akan dikembangkan menggunakan metode kecerdasan buatan lainnya

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

scholarly journals MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE

2019 ◽  
Vol 59 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Erol Can
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Multilevel Inverter ◽  
Logic Control ◽  
Multi Level

A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

Small Satellite Modelling and Three-Axis Magnetorquer-Based Stabilisation Using Fuzzy Logic Control

2012 ◽  
Vol 152-154 ◽  
pp. 1639-1644
Author(s):  
Amirhossein Asadabadi ◽  
Amir M. Anvar
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Control ◽  
Control Method ◽  
Small Satellite ◽  
Electromagnetic Actuation ◽  
Small Satellites ◽  
Logic Control ◽  
Electromagnetic Coils ◽  
Different Types

Recently small satellites have become increasingly popular because of their ability to provide educational institutes with the chance to design, construct, and test their spacecraft from beginning to the possible launch due to the low launching cost and development of microelectronics (Figure 1). Clearly, using only electromagnetic coils instead of different types of actuators will serve the purpose of weight reduction where every grams counts. But some restrictions described in the paper limit utilising only “Electromagnetic” actuation for 3D stabilisation and adversely affects the efficiency of the controller. However, there are some theories developed recently that have made the aforementioned purpose feasible. In this paper a new control method based on Fuzzy Logic Control (FLC) is presented that keeps the satellite in desired conditions only by electromagnetic coils. More precisely, an approach of Fuzzy control which is incorporated with electromagnetic actuation is presented for the in-orbit attitude control of a small satellite. The design is developed to stabilize the spacecraft against disturbances with a three-axis stabilizing capability. The paper also describes the required hardware and the design and development of the magnetic torquers.

Implementing Fuzzy Logic in the Control of a Biologically Inspired Robotic Cat Leg

2008 ◽  
Author(s):  
Anthony L. Crawford ◽  
Dean B. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Vertical Displacement ◽  
Skeletal Structure ◽  
Governing Equations ◽  
Biologically Inspired ◽  
Logic Control ◽  
Forcing Function ◽  
Force Range

This research discusses the implementation of a fuzzy logic control system to drive the movement of a simplified cat leg model. The system’s movement in this paper addresses a planar motion where the model experiences a fixed horizontal velocity and a harmonic vertical displacement. The fuzzy logic (FL) controller applies membership functions to fuzzify the position and velocity errors and applies height defuzzification to generate the time dependant forcing function for the system’s horizontal and vertical governing equations. A PID controller is also applied as a benchmark for this research. Both controllers are optimized using the simplex method for which the FL controller performed just as well as the PID controller with more promise of accounting for the nonlinear influences that were neglected in this simplified cat leg model and requiring actuators with a lower required force range. This research provides the skeletal structure for which an effective total controller can be built on.

scholarly journals RANCANG BANGUN FERMENTOR YOGURT DENGAN SISTEM KONTROL LOGIKA FUZZY MENGGUNAKAN MIKROKONTROLER ATMEGA32 (Yogurt Fermenter Design with Fuzzy Logic Control System Using Microcontroller ATMega32)

2015 ◽  
Vol 34 (04) ◽  
pp. 456
Author(s):  
Dimas Firmanda Al Riza ◽  
Retno Damayanti ◽  
Yusuf Hendrawan
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Temperature Control ◽  
Intelligent Control ◽  
Fuzzy Logic Control ◽  
Fermentation Process ◽  
Linear Time ◽  
Intelligent Control System ◽  
Logic Control ◽  
System Method

Yogurt is milk fermented product that becomes popular recently. In yogurt processing, fermenter is the main device. Lactobacillus sp. and Streptococcus sp. are two probiotic bacteria species that are common to be used in yogurt fermentation process. Both bacteria grow well in a specific range of temperature between 40-45 C, so temperature control in fermenter operational becomes one of the important things to ensure speed and quality of fermentation process. Fermentation process is a process with high degree of uncertainty and categorized as non-linear time invariant system. Thus, classical control system method is difficult to be implemented. To overcome this issue, intelligent control system can be implemented to yogurt’s fermenter temperature control. One of intelligent control system method that can be implemented is fuzzy logic-based control system. In this study, fuzzy control system has been designed andimplemented for fermenter temperature control. Control system algorithm is integrated in ATMega16 (for On-Off logic control) and ATMega32 (for Fuzzy Logic control) microcontrollers. Experimental results of fermenter control system shows that temperature profile of fermenter with fuzzy logic control system is more stable by settling time around an hour and 15 minutes and error average of -0.36 oC. Fermentation process for 16 hours with fuzzy logic controller produce yogurt with pH value of 3.66, total number of Lactobacillus sp. is 4.85 x 10 cfu/mL and Streptococcus sp. is1.34 x 106 cfu/mL.Keywords: Fermentation, yogurt, cow milk, fuzzy, temperature control ABSTRAKYogurt merupakan produk olahan susu terfermentasi yang akhir-akhir ini mulai banyak disukai oleh masyarakat. Pada pengolahan susu menjadi yogurt, fermentor digunakan sebagai alat utama. Lactobacillus sp. dan Streptococcus sp. merupakan dua spesies bakteri yang biasa digunakan dalam proses fermentasi yogurt. Kedua jenis bakteri ini tumbuhdengan baik pada suhu yang spesifik yaitu antara 40–45 C, sehingga pengendalian suhu pada operasi fermentor merupakan hal yang penting agar proses fermentasi dapat berjalan secara cepat dan baik. Proses fermentasi merupakan proses yang memiliki tingkat ketidakpastian yang tinggi dan merupakan sistem non-linear time variant, sehinggadesain sistem kontrol klasik akan sulit untuk diterapkan. Untuk mengatasi hal ini sistem kontrol cerdas dapat untuk diimplementasikan pada pengendalian suhu fermentor yogurt. Salah satu dari metode sistem kontrol cerdas yang dapat digunakan adalah sistem kontrol dengan logika fuzzy. Pada penelitian ini telah dilakukan rancang bangun sistempengendalian suhu berbasis algoritma fuzzy pada fermentor yogurt. Algoritma sistem kendali diintegrasikan dalam mikrokontroler ATMega16 (untuk logika ON-OFF) dan ATMega32 (untuk logika fuzzy). Hasil uji sistem pengendalian suhu fermentor menunjukkan bahwa dengan menggunakan algoritma fuzzy sistem pengendalian lebih stabil dengansettling time selama 1 jam 20 menit dan rata-rata error sebesar -0,36 oC. Proses fermentasi selama 16 jam menggunakan fermentor dengan kontroler fuzzy menghasilkan yogurt dengan pH sebesar 3,66, jumlah mikroba Lactobacillus sp. sebanyak 4,85 x 108cfu/mL, dan Streptococcus sp. sebanyak 1,34 x 10 6 cfu/mL.Kata kunci: Fermentasi, yogurt, susu sapi, fuzzy, kontrol suhu

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