Neural networks in feedforward control of a robot arm driven by antagonistically coupled drives

Author

Milosavljevic, P. ; Bascarevic, N. ; Jovanovic, K. ; Kvascev, G.

Author_Institution

Fac. of Electr. Eng., Univ. of Belgrade, Belgrade, Serbia

fYear

2012

fDate

20-22 Sept. 2012

Firstpage

77

Lastpage

80

Abstract

The paper deals with a rapidly growing trend in robotics - anthropomimetics. Following a human paragon, bio-inspired control of the robot arm is presented using artificial neural networks. This work demonstrates results achieved by feedforward control comparing feedforward backpropagation networks and radial bases networks. Use of radial bases network prevails as an efficient tool to evade the exact mathematical modeling and conventional control of the complex mechanical system that is highly nonlinear and includes passive compliance.

Keywords

backpropagation; compliance control; manipulators; neurocontrollers; nonlinear control systems; radial basis function networks; antagonistically coupled drive; anthropomimetics; artificial neural network; bio-inspired control; complex mechanical system; feedforward backpropagation network; feedforward control; highly nonlinear system; human paragon; mathematical modeling; passive compliance; radial bases network; robot arm; robotics; Artificial neural networks; Elbow; Feedforward neural networks; Joints; Robots; Training; antagonistic drives; humanoid robot; radial basis networks; robot control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on

Conference_Location

Belgrade

Print_ISBN

978-1-4673-1569-2

Type

conf

DOI

10.1109/NEUREL.2012.6419967

Filename

6419967