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.

Learning Feedback Linearization Control Without Torque Measurements / Capotondi, Marco; Turrisi, Giulio; Gaz, Claudio Roberto; Modugno, Valerio; Oriolo, Giuseppe; De Luca, Alessandro. - (2020). ((Intervento presentato al convegno I-RIM 2020 (2nd Italian Conference on Robotics and Intelligent Machines) tenutosi a Virtual [10.5281/zenodo.4781489].

Learning Feedback Linearization Control Without Torque Measurements

Marco Capotondi;Giulio Turrisi;Claudio Gaz;Valerio Modugno;Giuseppe Oriolo
;
Alessandro De Luca
2020

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1482499
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