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Filtered Sliding Mode Tracking Controller for Robot Joint Using Linear Matrix Inequalities Design

  • Lotfi Mostefai*
  • , Brahim Brahmi
  • , Angel Valera
  • , Valls Marina
  • *Corresponding author for this work

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

Abstract

In this paper, a Linear Matrix Inequalities (LMI) design of a filtered sliding mode controller is proposed, and applied to a robot joint for tracking control. The robot joint dynamics are linearised through the compensation of nonlinear terms (i.e. frictions, gravity, and coriolis torques), and a filtered control action is designed in order to prevent the chattering problem. The optimal control design using LMI technique can then be applied to the combined low pass filter and robot joint system, ensuring the stability of the closed loop. The proposed method is evaluated through numerical simulations of two degrees of freedom (2-DOF) end-effector type therapeutic robot, showing the robustness and performances of the controlled system under tracking task.

Original languageEnglish
Title of host publication13th International Conference on Intelligent Control and Information Processing, ICICIP 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages323-328
Number of pages6
ISBN (Electronic)9798331516147
DOIs
StatePublished - 2025
Externally publishedYes
Event13th International Conference on Intelligent Control and Information Processing, ICICIP 2025 - Hybrid, Muscat, Oman
Duration: 6 Feb 202511 Feb 2025

Publication series

Name13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

Conference

Conference13th International Conference on Intelligent Control and Information Processing, ICICIP 2025
Country/TerritoryOman
CityHybrid, Muscat
Period6/02/2511/02/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

  • Filtered SMC
  • LMI design
  • joint robot tracking control
  • therapeutic robot

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Control and Optimization
  • Modeling and Simulation

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