Neural network impedance force control of robot manipulator
Seul Jung
Hsia, T.C.
Robotics & Comput. Intelligence Lab., Chungnam Nat. Univ., Taejon;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Jun 1998
Volume: 45,
Issue: 3
On page(s): 451-461
ISSN: 0278-0046
References Cited: 19
CODEN: ITIED6
INSPEC Accession Number: 5933769
Digital Object Identifier: 10.1109/41.679003
Current Version Published: 2002-08-06
Abstract
The performance of an impedance controller for robot force
tracking is affected by the uncertainties in both the robot dynamic
model and environment stiffness. The purpose of this paper is to improve
the controller robustness by applying the neural network (NN) technique
to compensate for the uncertainties in the robot model. NN control
techniques are applied to two impedance control methods: torque-based
and position-based impedance control, which are distinguished by the way
of the impedance functions being implemented. A novel error signal is
proposed for the NN training. In addition, a trajectory modification
algorithm is developed to determine the reference trajectory when the
environment stiffness is unknown. The robustness analysis of this
algorithm to force sensor noise and inaccurate environment position
measurement is also presented. The performances of the two NN impedance
control schemes are compared by computer simulations. Simulation results
based on a three-degrees-of-freedom robot show that highly robust
position/force tracking can be achieved in the presence of large
uncertainties and force sensor noise
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