DocumentCode :
309391
Title :
Neural network hybrid position/force control
Author :
Connolly, Thomas H. ; Pfeiffer, Friedrich
Author_Institution :
Lehrstuhl B fur Mechanik, Tech. Univ. Munchen, Germany
Volume :
1
fYear :
1993
fDate :
26-30 Jul 1993
Firstpage :
240
Abstract :
The authors extend the application of a multilayered feedforward network to the hybrid position/force control problem. Using the measured positions and forces during an assembly task as inputs to a neural network, the necessary selection matrix and artificial constraints can be computed by the network. The authors use the peg-in-the-hole insertion problem to demonstrate their method. The neural network hybrid position/force controller is shown to correctly switch to the required position and force control modes and to recall the desired positions and forces required for each subcontrol task
Keywords :
multilayer perceptrons; artificial constraints; assembly task; hybrid position/force control; multilayered feedforward network; peg-in-the-hole insertion problem; selection matrix; Artificial neural networks; Control systems; Force control; Force measurement; Intelligent robots; Job shop scheduling; Neural networks; Optimal scheduling; Position measurement; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems '93, IROS '93. Proceedings of the 1993 IEEE/RSJ International Conference on
Conference_Location :
Yokohama
Print_ISBN :
0-7803-0823-9
Type :
conf
DOI :
10.1109/IROS.1993.583104
Filename :
583104
Link To Document :
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