Title
Robust Humanoid Control Using A Qp Solver With Integral Gains
Abstract
We propose a control framework for torque controlled humanoid robots that efficiently minimizes the tracking error in a Quadratic Programming (QP) formulated as multi-objective weighted tasks with constraints. It results in an optimal dynamically-feasible reference that can be tracked robustly, with exponential convergence, without joint torque feedback, in the presence of non modelled torque bias and low-frequency bounded disturbances. This is achieved by introducing integral gains in a Lyapunov-stable torque control, which exploit the passivity properties of the dynamical model of the robot and their effect on the dynamic constraints of the QP solver. The robustness of this framework is demonstrated in simulation by commanding our robot, the HRP-5P, to achieve simultaneously several objectives in the configuration and the Cartesian spaces, in the presence of non-modeled static and kinetic joint friction, as well as an uncertain torque scale.
Year
DOI
Venue
2018
10.1109/IROS.2018.8593417
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Keywords
Field
DocType
Robust control, Torque control, Passivity, Quadratic programming, Humanoid robots
Convergence (routing),Torque,Computer science,Control theory,Robustness (computer science),Control engineering,Quadratic programming,Solver,Robust control,Tracking error,Humanoid robot
Conference
ISSN
Citations 
PageRank 
2153-0858
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
R. Cisneros Limon183.64
Mehdi Benallegue23010.89
Abdelaziz Benallegue316417.91
Mitsuharu Morisawa464749.09
Hervé Audren591.55
Pierre Gergondet6634.70
Adrien Escande727322.91
Abderrahmane Kheddar81191101.66
Fumio KANEHIRO92304204.18