Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model
| Main Authors: | Alberto Dalla Libera, Fabio Amadio, Ruggero Carli, Diego Romeres |
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| Format: | Proceeding Journal |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://youtu.be/W6QWMtSQ_0c
https://www.youtube.com/watch?v=_afoOxaxLJI |
Daftar Isi:
- In this paper, we analyze the implementation of feedback linearization control scheme based on full data-driven inverse dynamics models. We made no use of physical models in the definition of the inverse dynamics, that has been learned entirely from previously recorded data via Gaussian Process Regression (GPR). The resulting controller has been tested in a simulated 7 dof manipulator to solve a trajectory tracking problem. Different kernel functions have been tested, in particular we analyzed the performance obtained by Squared Exponential (SE) kernel and the recently introduced Geometrically Inspired Polynomial (GIP) kernel. Results show that GIP obtains better tracking precision and it is more robust w.r.t. the presence of an initial tracking errors. On the contrary, poor generalization properties of SE kernel deeply undermine control performance when the robot is located far from the poses seen during training