Learning Feedback Linearization Control Without Torque Measurements

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Learning Feedback Linearization Control Without Torque Measurements

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

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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Title :

Learning Feedback Linearization Control Without Torque Measurements

Contributors:

Marco Capotondi (author) / Giulio Turrisi (author) / Claudio Gaz (author) / Valerio Modugno (author) / Giuseppe Oriolo (author) / Alessandro De Luca (author) / Capotondi, Marco / Turrisi, Giulio / Gaz, CLAUDIO ROBERTO / Modugno, Valerio

Publication date :

2020-01-01

DOI :

https://doi.org/10.5281/zenodo.4781489

Type of media :

Conference paper

Type of material :

Electronic Resource

Language :

English

Keywords :

Robot Learning , Feedback Linearization , Gaussian Process Regression , Underactuated Robot , Optimal Control