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Neural network based iterative learning controller for robot manipulators

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2 Author(s)
Yubin Gong ; Dept. of Autom., Tsinghua Univ., Beijing, China ; Pingfan Yan

An efficient neural network based learning control scheme is proposed to solve the trajectory tracking controI problem of robot manipulators. The proposed approach has four distinctive characteristics: 1) good tracking performance can be achieved during the first learning trial; 2) learning algorithm for adjusting neural network weights is independent of the manipulator dynamic model, thus displays strong robustness to torque disturbances and model parameter uncertainty; 3) no acceleration measurement or estimation is needed; and 4) real-time implementation with a higher sampling rate is readily possible. Simulation results on a 3 degree-of-freedom manipulator are presented to show its validity

Published in:

Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on  (Volume:1 )

Date of Conference:

21-27 May 1995