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Learning feedback linearization control without torque measurements

  • Marco Capotondi
  • , Giulio Turrisi
  • , Claudio Gaz
  • , Valerio Modugno
  • , Giuseppe Oriolo
  • , Alessandro De Luca

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

    Abstract

    Feedback Linearization (FL) allows the best control performance in executing a desired motion task when an accurate dynamic model of a fully actuated robot is available. However, due to residual parametric uncertainties and unmodeled dynamic effects, a complete cancellation of the nonlinear dynamics by feedback is hardly achieved in practice. In this paper, we summarize a novel learning framework aimed at improving online the torque correction necessary for obtaining perfect cancellation with a FL controller, using only joint position measurements. We extend then this framework to the class of underactuated robots controlled by Partial Feedback Linearization (PFL), where we simultaneously learn a feasible trajectory satisfying the boundary conditions on the desired motion while improving the associated tracking performance.
    Original languageEnglish
    DOIs
    Publication statusPublished - 10 Dec 2020
    Event2nd Italian Conference on Robotics and Intelligent Machines (I-RIM) - Rome, Italy (Held online)
    Duration: 10 Dec 2020 → 12 Dec 2020

    Conference

    Conference2nd Italian Conference on Robotics and Intelligent Machines (I-RIM)
    Period10/12/20 → 12/12/20

    Bibliographical note

    Note: Published in: Allotta, Benedetto, Chiara Carrozza, Maria, Menegatti, Emanuele, and Oriolo, Giuseppe (eds.) (2020) 2020 I-RIM Conference ISBN 9788894580518

    Organising Body: The Institute for Robotics and Intelligent Machines

    Keywords

    • Computer science and informatics

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    • 1 Conference contribution

    • Learning feedback linearization control without torque measurements

      Capotondi, M., Turrisi, G., Gaz, C., Modugno, V., Oriolo, G. & De Luca, A., 10 Dec 2020, Published in: Allotta, Benedetto, Chiara Carrozza, Maria, Menegatti, Emanuele, and Oriolo, Giuseppe (eds.) (2020) 2020 I-RIM Conference ISBN 9788894580518 Organising Body: The Institute for Robotics and Intelligent Machines Organising Body: The Institute for Robotics and Intelligent Machines.

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

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