A Comparative Study of PD, LQR and MPC on Quadrotor Using Quaternion Approach

Maidul Islam, Mohamed Okasha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

This study addresses the performances of three different controllers Proportional-Derivative (PD), Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) on a quaternion orientation based quadrotor and determines their suitability for different applications. As these controllers are widely being used in quadrotor platform with Euler-angle orientation, alternatively they are evaluated with quaternion orientation because this orientation can ensure singularity-free flight. In order to compare the performances of the controllers, performance indices like tracking error, considering RMSE method and control effort, using norm of control inputs have been considered. Based on the comparison, it is found that MPC is suitable for outdoor applications while PD and LQR can be best-fitted for indoor applications. MATLAB and Simulink environment has been considered in order to evaluate their performances.

Original languageEnglish
Title of host publication2019 7th International Conference on Mechatronics Engineering, ICOM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728129716
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event7th International Conference on Mechatronics Engineering, ICOM 2019 - Putrajaya, Malaysia
Duration: Oct 30 2019Oct 31 2019

Publication series

Name2019 7th International Conference on Mechatronics Engineering, ICOM 2019

Conference

Conference7th International Conference on Mechatronics Engineering, ICOM 2019
Country/TerritoryMalaysia
CityPutrajaya
Period10/30/1910/31/19

Keywords

  • Control Effort
  • LQR
  • MPC
  • Performance Index
  • PID
  • Trajectory Tracking

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Automotive Engineering

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