Скачать или смотреть NN Learning of Robot Dynamic Uncertainties & Observer-Based External Disturbance Estimation

Estimation of dynamic uncertainties is a critical and
fundamental problem when designing a control system for a
robot. During robot-environment interaction, in addition to
the internal dynamic model uncertainties, the external
environment-exerted force will also enter the dynamics. For
robot impedance control, an exact dynamic model of the
robot is needed but usually not available. It has been
shown that integrating an impedance controller with a
disturbance observer can achieve accurate impedance
control. However, it works only for robots in free motion
but not robot-environment interaction. Although a
disturbance observer is able to accurately estimate the
dynamic uncertainties, the estimation is lumped
uncertainties that contain all uncertainty sources
including both the internal and the external disturbances.
Without separating these two parts, the method of combining
an impedance controller and an observer will result in the
human-applied force being canceled instead of interacting
with the robot. To solve this problem in this paper, we
propose a framework for learning the internal disturbances
and separating the external disturbances by integrating
three entities: an impedance controller, a neural network
(NN) model, and a disturbance observer. In the framework,
the impedance controller provides compliant robot behavior,
while the observer captures the lumped uncertainties, and
the NN learns to separate the external disturbances.
Simulation results of an application scenario with an
obstructive virtual fixture demonstrate the effectiveness
of the proposed framework.

See our lab website for more details and a copy of the paper: http://www.ece.ualberta.ca/~tbs (go to Publications).