An online learning procedure for feedback linearization control without torque measurements

  • M. Capotondi
  • , G. Turrisi
  • , C. Gaz
  • , V. Modugno
  • , G. Oriolo
  • , A. De Luca

    Research output: Contribution to conference › Paper › peer-review

    Abstract

    By exploiting an a-priori estimate of the dynamic model of a manipulator, it is possible to command joint torques which ideally realize a Feedback Linearization (FL) controller. The exact cancellation may nevertheless not be achieved due to model uncertainties and possible errors in the estimation of the dynamic coefficients. In this work, an online learning scheme for control based on FL is presented. By reading joint positions and joint velocities information only (without the use of any torque measurement), we are able to learn those model uncertainties and thus achieve perfect FL control. Simulations results on the popular KUKA LWR iiwa robot are reported to show the quality of the proposed approach.
    Original languageEnglish
    Publication statusPublished - 30 Oct 2019
    Event3rd Conference On Robot Learning (CoRL 2019) - Osaka, Japan
    Duration: 30 Oct 2019 → 2 Nov 2019

    Conference

    Conference3rd Conference On Robot Learning (CoRL 2019)
    Period30/10/19 → 2/11/19

    Bibliographical note

    Note: Published in: Kaelbling, L.P., Kragic, D. and Sugiura, K. (eds.) (2020) PMLR Proceedings of Machine Learning Research: Vol. 100: Conference on Robot Learning: ML Research Press. pp. 1359-1368, ISSN (online) 2640-3498

    Organising Body: International Foundation of Robotics Research (IFRR),

    Keywords

    • Computer science and informatics

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    • 1 Conference contribution

    • An online learning procedure for feedback linearization control without torque measurements

      Capotondi, M., Turrisi, G., Gaz, C., Modugno, V., Oriolo, G. & De Luca, A., 30 Oct 2019, Published in: Kaelbling, L.P., Kragic, D. and Sugiura, K. (eds.) (2020) PMLR Proceedings of Machine Learning Research: Vol. 100: Conference on Robot Learning: ML Research Press. pp. 1359-1368, ISSN (online) 2640-3498 Organising Body: International Foundation of Robotics Research (IFRR), Organising Body: International Foundation of Robotics Research (IFRR),.

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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