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Dettaglio pubblicazione

2020, Proceedings of I-RIM 2020, Pages -

Learning Feedback Linearization Control Without Torque Measurements (04b Atto di convegno in volume)

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

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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