Abstract: In this paper, an adaptive control method for hybrid position/force
control of robot manipulators, based on neuro-fuzzy modeling, is presented.
Since force control involves applying certain amount of force on the surface of
an object, it is important to consider the friction force between end-effector
and surface into account. In order to compensate this friction force, a robust
and adaptive neuro-fuzzy compensator will be designed and incorporated into the
close-loop system. Moreover, to determine stiffness coefficient of surface, an
on-line estimator will be designed for more precise computation of the desired
force. Due to the adaptive neuro-fuzzy modeling, the proposed controller is
independent of robot dynamics, since the free parameters of the neuro-fuzzy
controller are adaptively updated to cope with changes in the system and the
environment. As a result, the tracking error, both for position and force, will
always remain small. Also, the stability of the controller is guaranteed, since
the adaptation law is based on Lyapunov theory. In addition to that, the
convergence of the adaptive parameters will be proved in this paper. The
simulation results show good performance of the proposed controller as compared
with other conventional control schemes for robot manipulators such as computed
torque method.
