M.Sc. Tezi Görüntüleme

Student: Reza MIRZAPOUR
Supervisor: Prof. Dr. İsmail Hakkı ALTAŞ
Department: Elektrik-Elektronik Müh.
Institution: Graduate School of Natural and Applied Sciences
University: Karadeniz Technical University Turkey
Title of the Thesis: A Soft Computing Based Optimization Algorithm for an Adaptive Fuzzy Logic Controller
Level: M.Sc.
Acceptance Date: 10/6/2014
Number of Pages: 63
Registration Number: i2800

      Nowadays, speed control is essential for many applications. DC motor has priority for many speed control technics due to its features like it is easy to control, generally it has small structure and it has lots of different types to find proper one for the purpose of implementation. Not only choosing appropriate motor for the application, but also determining with which controller it should be controlled, is important. To determe this, it should be take into consideration the fact that conventional methods are not adequate for many applications and new technics are needed. Because conventional controllers can only respond to linear applications, but it is not sufficient for non-linear applications and it can get affected easily with the environmental distrubances and it can have difficulties to track for for different parameters. Those reasons lead many users to intelligent controllers like Fuzzy Logic and Artificial Neural Network.

In this study, DC motor is controlled with a Fuzzy Logic controller whose membership functions are tuned by Artificial Neural Network. The weights of Artificial Neural Network in this study is optimised with Genetic Algorithm. This thesis is fomed in three steps. In first steps, general knowledge which should be known are given with the title of introduction. In second part, general function of the controller which is suggested is described. Last part is for the Matlab/SIMULINK applications. In this part, firstly DC motor is controlled with PID in order tomake comparision between conventional controllers and intelligent controllers, then it is controlled with some intelligent controllers such as Fuzzy Logic and Artificial Neural Network and lastly it is controlled with proposed controller. Related outputs are compared in the conclusion of the thesis.

      Key Words: Direct current motor, Fuzzy Logic, Artificial Neural Network, Genetic Algorithm