scholarly journals Optimal Fuzzy Logic Control for Temperature Control Based on Social Spider Optimization

2020 ◽  
Vol 745 ◽  
pp. 012099
Author(s):  
Tahani Ghanim ◽  
Ahmed R. Ajel ◽  
Amjad j. Humaidi
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Control ◽  
Social Spider ◽  
Logic Control ◽  
Social Spider Optimization

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.

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

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

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.

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