Neural network impedance force control of robot manipulator

被引:105
|
作者
Jung, S [1 ]
Hsia, TC
机构
[1] Chungnam Natl Univ, Dept Mechatron Engn, Robot & Computat Intelligence Lab, Taejon 305764, South Korea
[2] Univ Calif Davis, Dept Elect & Comp Engn, Robot Res Lab, Davis, CA 95616 USA
关键词
impedance force control; neural network controllers; robot manipulators;
D O I
10.1109/41.679003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:451 / 461
页数:11
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