scholarly journals Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Evolutionary Algorithm ◽  
Electric Motor ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Computer Interface ◽  
Rule Base ◽  
Learning Stage ◽  
Interface Circuit ◽  
Termination Criteria ◽  
Optimal Adjustment

This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit.<div>Note : This article has been accepted for publication in a future issue of ELECTRICA journal, it is now in the early view. </div><div>Citation information: </div><div>M. Bulut, "Optimal Adjustment of Evolutionary Algorithm-based Fuzzy Controller for Driving Electric Motor with Computer Interface", Electrica, August 5, 2021. DOI: 10.5152/electrica.2021.21033.</div>

scholarly journals Optimal Adjustment of Fuzzy Controller Based Evolutionary Algorithms For Driving Electric Motor with Computer Interface

2020 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Rule Base ◽  
Learning Stage ◽  
Interface Circuit ◽  
Termination Criteria ◽  
Optimum Parameters ◽  
Optimal Adjustment ◽  
Rule Bases

This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit.

scholarly journals Optimal Adjustment of Fuzzy Controller Based Evolutionary Algorithms For Driving Electric Motor with Computer Interface

2020 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Rule Base ◽  
Learning Stage ◽  
Interface Circuit ◽  
Termination Criteria ◽  
Optimum Parameters ◽  
Optimal Adjustment ◽  
Rule Bases

This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit.

Fuzzy Controller Approach for DC Motor Control on Fishing Rod

2015 ◽  
Vol 2 (1) ◽  
pp. 20-28
Author(s):  
Emmanuel Ade Crisna Putra ◽  
Houtman P. Siregar
Keyword(s):  
Fuzzy Logic ◽  
Transfer Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Dc Motor ◽  
Step Response ◽  
Rule Base ◽  
Dc Motor Control ◽  
Automation Control

In this paper described the usable and effectiveness of automation control by using fuzzy logic controller forcontrolling the speed of DC motor that will be used on string roller of fishing rod. The transfer function of DCmotor has been obtained. For transfer function, the load of DC motor will be acted as input, and the output is thevelocity of DC motor. The fuzzy rule base then created by trial and error. The step response between fuzzy logiccontroller and without using fuzzy logic controller then obtained and compared. As a result, the fuzzy logic hassuccessfully reduced the overshoot of step response.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

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.

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