DocumentCode :
288722
Title :
Experiments in feedforward shaping control of direct-drive robot using RBF network
Author :
Torfs, Dirk ; Gorinevsky, Dimitry ; Goldenberg, Andrew
Author_Institution :
Robotics & Autom. Lab., Toronto Univ., Ont., Canada
Volume :
5
fYear :
1994
fDate :
27 Jun-2 Jul 1994
Firstpage :
2737
Abstract :
This paper considers a new approach to manipulator trajectory tracking. Feedforward control shape is computed with a radial basis function (RBF) network as a result of approximation over task parameter domain. The task parameters comprise coordinates of the initial and final point of the trajectory with motion time being fixed. The RBF network is trained using the feedforward shapes obtained with a learning control algorithm. The paper demonstrates experimental implementation of all steps of the algorithm for trajectory tracking in fast (1.25 s) motions of a direct-drive industrial robot AdeptOne. High performance is achieved in experiment opening an avenue for practical applications of the approach
Keywords :
feedforward neural nets; industrial robots; learning (artificial intelligence); robots; AdeptOne; direct-drive industrial robot; feedforward shaping control; learning control algorithm; manipulator trajectory tracking; radial basis function network; Intelligent networks; Manipulator dynamics; Radial basis function networks; Robot control; Robot kinematics; Robotics and automation; Service robots; Shape control; Tracking; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
Type :
conf
DOI :
10.1109/ICNN.1994.374663
Filename :
374663
Link To Document :
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