Data-Driven Disturbance Compensation for DC Motors

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

1 Scopus citations

Abstract

This paper presents applications of recently developed data-driven disturbance observer [1] for DC motor control. The proposed approach enables effective compensation for external disturbances, allowing for robust control even in the presence of noise or other interfering factors. By utilizing data-driven techniques, the disturbance observer learns the system dynamics from the behavior of the system to accurately estimate and compensate for disturbances. Experimental results demonstrate the effectiveness of the proposed data-driven observer in mitigating the impact of disturbances and improving the overall control performance of the motor system.

Original languageEnglish
Title of host publication23rd International Conference on Control, Automation and Systems, ICCAS 2023
PublisherIEEE Computer Society
Pages359-360
Number of pages2
ISBN (Electronic)9788993215274
DOIs
StatePublished - 2023
Event23rd International Conference on Control, Automation and Systems, ICCAS 2023 - Yeosu, Korea, Republic of
Duration: 17 Oct 202320 Oct 2023

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Conference

Conference23rd International Conference on Control, Automation and Systems, ICCAS 2023
Country/TerritoryKorea, Republic of
CityYeosu
Period17/10/2320/10/23

Bibliographical note

Publisher Copyright:
© 2023 ICROS.

Keywords

  • Behavioral approach
  • Data-driven control
  • Disturbance observer

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