Authors:
Nirmal Giftsun
;
Andrea Del Prete
and
Florent Lamiraux
Affiliation:
Laboratory for Analysis and Architecture of Systems, France
Keyword(s):
Humanoid Robots, Robust Control, Inverse-Dynamics Control, Optimization and Optimal Control.
Related
Ontology
Subjects/Areas/Topics:
Humanoid Robots
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Robotics and Automation
Abstract:
Model-based control has become more and more popular in the legged robots community in the last ten
years. The key idea is to exploit a model of the system to compute precise motor commands that result
in the desired motion. This allows to improve the quality of the motion tracking, while using lower gains,
leading so to higher compliance. However, the main flaw of this approach is typically its lack of robustness to
modeling errors. In this paper we focus on the robustness of inverse-dynamics control to errors in the inertial
parameters of the robot. We assume these parameters to be known, but only with a certain accuracy. We
then propose a computationally-efficient optimization-based controller that ensures the balance of the robot
despite these uncertainties. We used the proposed controller in simulation to perform different reaching tasks
with the HRP-2 humanoid robot, in the presence of various modeling errors. Comparisons against a standard
inverse-dynamics controller through h
undreds of simulations show the superiority of the proposed controller
in ensuring the robot balance.
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