Conference paper Open Access
Learning Feedback Linearization Control Without Torque Measurements
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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Learning Feedback Linearization Control Without Torque Measurements-z9beqxr1wAk.mp4
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Learning Feedback Linearization Control Without Torque Measurements.pdf
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- Publication date:
- December 10, 2020
- DOI:
-
- Keyword(s):
- Robot Learning Optimal Control Feedback Linearization Gaussian Process Regression Underactuated Robots
- Meeting:
- None (Session Dynamics, Control and Learning III)
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International