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

Simulation for Temperature Control System of Dyeing Machine Based on Fuzzy Control

2011 ◽  
Vol 317-319 ◽  
pp. 1688-1692
Author(s):  
Min Ling Zhao ◽  
Guo Ping Li ◽  
Xiong Bo Ze ◽  
Cheng Kai Ji
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Decisive Role ◽  
Temperature Control System ◽  
Logic Control

In the process of dyeing, the temperature control of dyeing machine plays a decisive role on the stand or fall quality of fabric. The establishment of the traditional PID controller’s parameters needs a lot of test, which brings many inconvenience.Therefore, it is proposed to control dyeing machine temperature by fuzzy controller. Based on the principle of fuzzy logic control, the model of the temperature control system of dyeing machine is built. At the same time, through the fuzzy logic toolbox in matlab software, fuzzy controller of temperature is designed. Then a comparative simulation of the temperature control system of dyeing machine with matlab has been accomplished. Through the analysis of the results, it is concluded that the temperature system can achieve the higher steady precision.

The Application Study of Temperature Control Strategy for Boiler

2013 ◽  
Vol 313-314 ◽  
pp. 462-465
Author(s):  
Guo Qiang Hou ◽  
Ping Lv
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Steam Temperature ◽  
Application Study ◽  
Fuzzy Control System ◽  
Logic Control

The series fuzzy control system is designed for boiler steam temperature based on the characteristics of boiler steam temperature control. The composition, the principles and the algorithm were presented in this paper. The corresponding fuzzy logic control system is simulated and analyzed with MATLAB.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Permanent Magnet Chatter Absorber with Fuzzy Logic Control

2009 ◽  
Vol 147-149 ◽  
pp. 290-295 ◽  
Author(s):  
Bogdan Broel-Plater ◽  
Stefan Domek ◽  
Arkadiusz Parus
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Control Algorithm ◽  
Permanent Magnets ◽  
Practical Experience ◽  
High Energy ◽  
Logic Control ◽  
Machine Operators

The paper deals with semi-active chatter absorber based on an electrodynamic transducer built around high-energy permanent magnets. Also, a fuzzy logic control system for the absorber control system has been designed. The principal advantage of fuzzy control is the possibility to implement practical experience gained by machine operators in the control algorithm. Hence, the possibility of factoring such quantities, as vibrations experienced by selected points of the machine-tool, and sound emitted by working machine into the analyzed chatter absorber fuzzy control system has been studied in the paper. The control system has been tested by way of simulation with the use of the process and cutting force models.

scholarly journals Reconfigurable fuzzy logic system for high-frame rate stereovision object tracking

2021 ◽  
Author(s):  
Oleg Samarin
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Object Tracking ◽  
Fuzzy Logic Control ◽  
Tracking System ◽  
Frame Rate ◽  
Disparity Map ◽  
High Frame Rate ◽  
Logic Control ◽  
Logic System

his study investigates the applicability of fuzzy logic control to high-frame rate stereovision object tracking. The technology developed in this work is based on utilizing a disparity map produced by the Stereovision Tracking System (STS) to identify the object of interest. The coordinates of the object are used by the fuzzy logic control system to provide rotation and focus control for object tracking. The fuzzy logic control was realized as a reconfigurable hardware module and implemented on Virtex-2 FPGA platform of the STS. The fuzzy reasoning was implemented as a reconfigurable look-up table residing in FPGA's internal memory. A set of software tools facilitating creation of loop-up table and reconfiguration of fuzzy logic control system was developed. Finally, the experimental prototype of the system was built and tested.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Electromagnetic Levitator at Space Power Institute, Auburn University

2000 ◽  
Author(s):  
Bogdan O. Ciocirlan ◽  
Dan B. Marghitu ◽  
David G. Beale ◽  
Ruel A. Overfelt
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Analytical Model ◽  
Lorentz Force ◽  
Fuzzy Logic Control ◽  
Vertical Motion ◽  
Electromagnetic Levitation ◽  
Auburn University ◽  
Logic Control ◽  
Electromagnetic Levitator

Abstract In this paper, the electromagnetic levitation instrument designed by Space Power Institute at Auburn University is analyzed. An analytical model to compute the Lorentz force that supports the specimen against gravity is proposed. The equation of vertical motion of the specimen is developed and linearized. A fuzzy logic control system designed to stabilize the motion of levitated specimens is also presented.

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