scholarly journals Sistem Cerdas untuk Inovasi Blender Control System Menggunakan Fuzzy Control System dengan Metode Mamdani

2018 ◽  
pp. 115
Author(s):  
Khomarudin Fahuzan ◽  
Uke Ralmugiz
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Rule Base ◽  
Inference Control ◽  
Fuzzy Control System ◽  
Hardness Level ◽  
A Value ◽  
Time Required

This research aims to establish a control system on blender by using fuzzy control system with mamdani method. In this study, researchers used input in the form of hardness level and volume of fruit to be blend, while the output is blend time (0 to 180 seconds) with assumption of constant blender velocity). Researchers used fuzzy inference control system with Mamdani method with some stages: fuzzification, inference, rule base, and defuzzification. Fuzzification changes the hardness of the fruit and the volume into a value. Inference created fuzzy output using pre-made rules. Defuzzification counted the time it takes to blend into output. Based on the results of the research, the results obtained for the sample of fruit with a level of hardness of 40%, and volume 4 (400 ml), in obtaining the minimum time required to smooth the fruit about 79 seconds. Thus the fuzzy control system can be used as an innovation to make the control system in blender. This system not only applies to blenders only, but also can be applied to other machines using fuzzy control system.

Study on Adaptive Fuzzy Control System of Settlement Technology

2014 ◽  
Vol 1061-1062 ◽  
pp. 904-907
Author(s):  
Xiang Ping Chen
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Control Method ◽  
Dynamic Control ◽  
Adaptive Fuzzy Control ◽  
Fuzzy Control System ◽  
Adaptive Fuzzy ◽  
The Mathematical Model ◽  
Clear Solution

Considering the production status of red mud at present, an adaptive fuzzy control system, according to fuzzy control and genetic algorithm, has been focused on. With the control of flocculants, the system fuzzy control clarity of clear solution. Adaptive neural network fuzzy inference theory is adopted to establish the mathematical model of controlled object "black box", and MATLAB for simulation, showing that the control method has good accuracy and dynamic control quality. Satisfy the requirements of practice work.

scholarly journals Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 752
Author(s):  
Dimitrios Kontogiannis ◽  
Dimitrios Bargiotas ◽  
Aspassia Daskalopulu
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Decision Tree ◽  
Fuzzy Control ◽  
Residential Buildings ◽  
Environmental Data ◽  
Weather Data ◽  
Series Data ◽  
Rule Base ◽  
Fuzzy Control System

Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration.

Fuzzy Control of a Closed-Die Hot-Forging Machine

1994 ◽  
Vol 208 (4) ◽  
pp. 239-248
Author(s):  
D T Pham ◽  
M H Wu
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Hot Forging ◽  
Inference Procedure ◽  
Fuzzy Control System ◽  
Fuzzy Variables ◽  
Input And Output

The development of a fuzzy control system for a closed-die hot-forging machine is described. Details of the input and output fuzzy variables and the fuzzy inference procedure are given. Results obtained using the fuzzy control system are presented. These demonstrate the ability of the system accurately to control the amount of energy delivered to the workpiece to keep it within narrow tolerances without overloading the die.

Researching on Fuzzy Control System of Jigging Discharge Based on PLC

2011 ◽  
Vol 201-203 ◽  
pp. 2028-2032
Author(s):  
Yu Chen
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Nonlinear Control ◽  
Common Sense ◽  
Control Strategy ◽  
Fuzzy Inference ◽  
Operational Experience ◽  
Fuzzy Control System ◽  
Fuzzy Language

Fuzzy control is a nonlinear control strategy based on fuzzy inference. It shows the people’s operational experience and common sense rules of inference through fuzzy language. For the jigging discharging system, its overall structure and the necessary hardware and software in the process of controlling are designed. And then, the fuzzy control of jigging discharging system is actualized based on S7-300 PLC. After on-site commissioning, the control system can meet the site requirements.

Fuzzy Control System of Water Level Based on Fuzzy Reasoning

2013 ◽  
Vol 385-386 ◽  
pp. 803-807
Author(s):  
Tao Han ◽  
Xiao Hui Chen
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Water Level ◽  
Fuzzy Inference ◽  
Fuzzy Variable ◽  
Fuzzy Reasoning ◽  
Fuzzy Control System ◽  
Whole Process ◽  
Input And Output ◽  
Fuzzy Description

This paper introduces a control system of water level based on fuzzy control, describes the whole process of fuzzy reasoning. In this control system, the fuzzy controller's input and output variables are determined, and the fuzzy variable description. Then the choice of rules and fuzzy description, establishing fuzzy relationship, and fuzzy inference, and defuzzification.

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