Learning feedback linearization control without torque measurements

Capotondi, Marco, Turrisi, Giulio, Gaz, Claudio, Modugno, Valerio, Oriolo, Giuseppe and De Luca, Alessandro (2020) Learning feedback linearization control without torque measurements. In: 2nd Italian Conference on Robotics and Intelligent Machines (I-RIM); 10-12 Dec 2020, Rome, Italy (Held online).


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.

Actions (Repository Editors)

Item Control Page Item Control Page