Payload estimation for a robotic system using unsupervised classification

L. Angel, J. Viola

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

    2 Scopus citations

    Abstract

    A robotic system may be affected by external disturbances and parametric uncertainness, which change its dynamical behavior. One of the most common disturbances is the payload variation that affects the control system performance. If the payload variation is known, its negative effects can be minimized adjusting the control system parameters. However, when the payload variation is unknown, the control system parameters cannot be adjusted appropriately. This paper proposes a methodology for the payload variation estimation for a robotic system using unsupervised classification techniques. BSAS, MBSAS and Kmeans algorithms were employed as clustering techniques. The Silhouette index and the standard deviation were employed as performance indexes to compare the classification algorithms. Results showed that Kmeans algorithm has a better performance for the payload variation classification.

    Original languageEnglish
    Title of host publication2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
    EditorsMiguel Altuve
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781509037971
    DOIs
    StatePublished - 14 Nov 2016
    Event21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 - Bucaramanga, Colombia
    Duration: 30 Aug 20162 Sep 2016

    Publication series

    Name2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

    Conference

    Conference21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
    Country/TerritoryColombia
    CityBucaramanga
    Period30/08/162/09/16

    Bibliographical note

    Publisher Copyright:
    © 2016 IEEE.

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