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dc.contributor.author Samuel, Kangwagye -
dc.contributor.author Haninger, Kevin -
dc.contributor.author Oh, Sehoon -
dc.contributor.author Lee, Chan -
dc.date.accessioned 2024-02-09T03:10:15Z -
dc.date.available 2024-02-09T03:10:15Z -
dc.date.created 2023-09-01 -
dc.date.issued 2023-06-30 -
dc.identifier.isbn 9781665476331 -
dc.identifier.issn 2159-6255 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/47921 -
dc.description.abstract In conventional robust motion control systems, disturbance observer (DOB) nominal models are designed with same order as the actual plant such that the nominal model directly cancels with the actual plant dynamics. However, for multi-DOF systems such as 6-DOF industrial robots, identifying the higher-order model is laborious. Moreover, there is a high risk of obtaining a nominal model with large deviation from the actual plant due to severe parameter uncertainty. Thus, a reduced-order nominal model is derived from the actual plant model and compared with the one which same order as the actual plant in this paper. The designed model is simple, easy to identify and implement. From the analyses and experiment results, DOB with the proposed nominal model is not affected by severe robot model uncertainty and show significant improvement in motion control performance in terms of transient response and tracking accuracy. © 2023 IEEE. -
dc.language English -
dc.publisher IEEE Robotics and Automation Society (RAS), IEEE Industrial Electronics Society (IES), ASME Dynamic Systems and Control Division (DSCD) -
dc.title Reduced-Order Nominal Model Design and Validation For Task Space DOB-Based Motion Control of An Industrial Robot -
dc.type Conference Paper -
dc.identifier.doi 10.1109/AIM46323.2023.10196253 -
dc.identifier.scopusid 2-s2.0-85168422578 -
dc.identifier.bibliographicCitation IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023, pp.1095 - 1101 -
dc.identifier.url https://aim2023.org/assets/aim2023-program-book-formated_Final.pdf -
dc.citation.conferencePlace US -
dc.citation.conferencePlace Seattle -
dc.citation.endPage 1101 -
dc.citation.startPage 1095 -
dc.citation.title IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023 -
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Department of Robotics and Mechatronics Engineering MCL(Motion Control Lab) 2. Conference Papers

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