Self-Correcting Quadratic Programming-Based Robot Control

نویسندگان

چکیده

Quadratic programming (QP)-based controllers allow many robotic systems, such as humanoids, to successfully undertake complex motions and interactions. However, these approaches rely heavily on adequately capturing the underlying model of environment robot’s dynamics. This assumption, nevertheless, is rarely satisfied, we usually turn well-tuned end-effector PD compensate for mismatches. In this article, propose augment traditional QP-based with a learned residual inverse dynamics (IDs) an adaptive control law that adjusts QP online account uncertainties unforeseen disturbances. particular, propose: 1) learning IDs using Gaussian Process linearizing it so can be incorporated inside QP-control optimization procedure 2) novel combination methods avoid manual tuning PID faster convergence in model. simulation, extensively evaluate our method several scenarios ranging from 7-degrees freedom (DoFs) manipulator tracking trajectory humanoid robot performing waving motion which used by controller one simulated world do not match (unmodeled dynamics). Finally, also validate approach physical where 7-DoFs arm performs tasks (mass, friction coefficients, etc.) fully known.

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ژورنال

عنوان ژورنال: IEEE transactions on systems, man, and cybernetics

سال: 2023

ISSN: ['1083-4427', '1558-2426']

DOI: https://doi.org/10.1109/tsmc.2023.3262954