prikaz prve stranice dokumenta Parametrizacija regulatora primjenom evolucijskih algoritama
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master's thesis
Parametrizacija regulatora primjenom evolucijskih algoritama
Rijeka: University of Rijeka, Faculty of Engineering, 2020. urn:nbn:hr:190:389078
Mavrinac, Marko
University of Rijeka
Faculty of Engineering
Department of Automation and Electronics
Section of Electronics, Robotics and Automation

Institutional repository: Repository of the University of Rijeka, Faculty of Engineering

Cite this document

Mavrinac, M. (2020). Parametrizacija regulatora primjenom evolucijskih algoritama (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:389078

Mavrinac, Marko. "Parametrizacija regulatora primjenom evolucijskih algoritama." Master's thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:389078

Mavrinac, Marko. "Parametrizacija regulatora primjenom evolucijskih algoritama." Master's thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:389078

Mavrinac, M. (2020). 'Parametrizacija regulatora primjenom evolucijskih algoritama', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 21 January 2025, https://urn.nsk.hr/urn:nbn:hr:190:389078

Mavrinac M. Parametrizacija regulatora primjenom evolucijskih algoritama [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2020 [cited 2025 January 21] Available at: https://urn.nsk.hr/urn:nbn:hr:190:389078

M. Mavrinac, "Parametrizacija regulatora primjenom evolucijskih algoritama", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:190:389078

Metadata
TitleParametrizacija regulatora primjenom evolucijskih algoritama
Title (english)Regulator parametrization using evolutionary algorithms
AuthorMarko Mavrinac
MentorZlatan Car (mentor)
Committee memberAlfredo Višković (predsjednik povjerenstva)
Committee memberZlatan Car (član povjerenstva)
Committee memberRene Prenc (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Automation and Electronics)
(Section of Electronics, Robotics and Automation)
Rijeka
Defense date and country2020-09-22, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Ovaj rad bavi se primjenom genetskog algoritma za problem optimizacije sustava regulacije DC motora s nezavisnom uzbudom po kutu rotora. Teoretski je opisan koncept genetskog algoritma i njegovih elemenata, sustava regulacije koji se optimizira. a uz to je opisana i njihova implementacija u Pythonu, dizajn grafičkog sučelja za prikaz rezultata i sučelja za usporedbu svojstava genetskih algoritama različitih parametara. Uz to, implementirane su odreĎene metode optimizacije uz pomoć kojih se znatno smanjuje vrijeme izvoĎenja algoritma, a izvršena je usporedba rezultata u MATLAB-u odnosno Simulinku kako bi se utvrdila vjerodostojnost matematičkog modela u Pythonu kao i vjerodostojnost rezultata optimizacije. Kako bi se utvrdio idealan skup parametara za rješenje problema optimizacije navedenog sustava regulacije, izvršena je usporedba genetskih algoritama bazirana na veličini populacije, vjerojatnosti mutacije, relativnoj toleranciji ocjene podobnosti i različitim metodama selekcije.
Abstract (english)
This paper analyzes application of genetic algorithm for the purpose of optimizing a control system with separately excited DC motor controlled according to the rotor angle. The theoretical concept of genetic algorithm and its elements has been described along with the concept of the control system being optimized. Also, Python implementation of the genetic algorithm, mathematical model of the control system, graphical user interface for result analysis and interface for genetic algorithm comparison have been described. Certain optimization methods used for noticeable execution time reduction have been implemented and comparison has been made between MATLAB and Python model simulation results to ensure credibility of optimization results given by the genetic algorithm. In order to determine an ideal set of parameters for this particular optimization problem, a comparison has been made between genetic algorithms based on population size, mutation probability, relative fitness score tolerance and different selection methods.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:389078
Study programmeTitle: Graduate University Study of Electrical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-08-27 13:09:18