LQGT Control Design Based on Firefly Algorithm optimization for Trajectory Tracking on Quadcopter

Sovia Prabaningtyas, Mardlijah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The quadcopter is very dynamic system, that to carry out a certain mission, it is necessary to apply control to it. In this paper, Linear Quadratic Gaussian Tracking control is designed to control rotational and lateral motions of quadcopter so that it can follow the predetermined trajectory. Determination of weighting matrices Q and R on LQGT control is formulated as an optimization problem and then will be solved using the firefly algorithm. The simulation results show that the use of the firefly algorithm in determining optimal weighting matrices Q and R are proven to be effective. The application of LQGT control on the system is able to make it follow the square and triangle trajectory with minimum average track error. There is a time lagging on both trajectories for x-position and y-position with an average of 0.59 seconds.

Original languageEnglish
Title of host publication2022 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publicationAdvanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-266
Number of pages6
ISBN (Electronic)9781665460811
DOIs
Publication statusPublished - 2022
Event23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022 - Virtual, Surabaya, Indonesia
Duration: 20 Jul 202221 Jul 2022

Publication series

Name2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding

Conference

Conference23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period20/07/2221/07/22

Keywords

  • LQGT
  • firefly algorithm
  • optimal control
  • optimization
  • quadcopter

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