Payload estimation for a robotic system using unsupervised classification

L. Angel, J. Viola

    Producción científica: Capítulo del libro/informe/acta de congresoPonencia publicada en las memorias del evento con ISBNrevisión exhaustiva

    2 Citas (Scopus)

    Resumen

    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.

    Idioma originalInglés
    Título de la publicación alojada2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
    EditoresMiguel Altuve
    EditorialInstitute of Electrical and Electronics Engineers Inc.
    ISBN (versión digital)9781509037971
    DOI
    EstadoPublicada - 14 nov. 2016
    Evento21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 - Bucaramanga, Colombia
    Duración: 30 ago. 20162 sep. 2016

    Serie de la publicación

    Nombre2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

    Conferencia

    Conferencia21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
    País/TerritorioColombia
    CiudadBucaramanga
    Período30/08/162/09/16

    Nota bibliográfica

    Publisher Copyright:
    © 2016 IEEE.

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