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Repetitive Trajectory Tracking of Autonomous Mobile Robots via SMC with Iterative Learning

  • Harbin Institute of Technology
  • School of Astronautics, Harbin Institute of Technology

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

Abstract

Aiming at the increasingly strict trajectory tracking accuracy requirements and the characteristics of repetitive tasks of autonomous mobile robots (AMR), this paper proposes a robust control strategy that combines iterative learning control (ILC) and adaptive sliding model control (ASMC) in parallel to handle unknown disturbances and model uncertainties. Specifically, ASMC is employed as the main controller to deal with the lumped disturbances/uncertainties, which ensures the stability and strong robustness of the system. A PD-type iterative learning controller is used as the auxiliary controller to further improve tracking performance in repetitive motion tasks. The experimental results show that the ASMC-ILC controller outperforms both the iterative learning control and sliding mode control algorithms in terms of control performance.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
EditorsRong Song
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1458-1463
Number of pages6
ISBN (Electronic)9798350316308
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Unmanned Systems, ICUS 2023 - Hefei, China
Duration: 13 Oct 202315 Oct 2023

Publication series

NameProceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

Conference

Conference2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Country/TerritoryChina
CityHefei
Period13/10/2315/10/23

Keywords

  • Adaptive sliding model control
  • autonomous mobile robots
  • iterative learning control
  • trajectory tracking

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