Modelling and experimental analysis two-wheeled self balance robot using PID controller

Aminu Yahaya Zimit, Hwa Jen Yap, Mukhtar Fatihu Hamza, Indrazno Siradjuddin, Billy Hendrik, Tutut Herawan

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

13 Scopus citations

Abstract

This research is aimed to design and implement Proportional Integral Derivative (PID) controller on Two-wheeled self-balance (TWSB) robot. The PID is used for the purpose of balancing the robot to stand still at upright position and to receive command via Bluetooth to follow the desired trajectory smoothly. The dynamic model of TWSB robot was developed using Lagrangian method. The PID gains were tuned until the optimum values are achieved. The Arduino based PID-controller was implemented on the TWSB robot in real world experiment. The experimental result shows the effectiveness of the proposed controller for stabilization and trajectory tracking control of TWSB robot.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings
EditorsEufemia Tarantino, Beniamino Murgante, David Taniar, Yeonseung Ryu, Carmelo M. Torre, Ana Maria Rocha, Osvaldo Gervasi, Sanjay Misra, Elena Stankova, Bernady O. Apduhan
PublisherSpringer Verlag
Pages683-698
Number of pages16
ISBN (Print)9783319951645
DOIs
StatePublished - 2018
Externally publishedYes
Event18th International Conference on Computational Science and Its Applications, ICCSA 2018 - Melbourne, Australia
Duration: 2 Jul 20185 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10961 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Computational Science and Its Applications, ICCSA 2018
Country/TerritoryAustralia
CityMelbourne
Period2/07/185/07/18

Keywords

  • Mathematical model
  • PID
  • Self-balanced robot
  • Trajectory tracking control

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