By Topic

Using neuroevolution for optimal impedance control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
de Gea, J. ; Robot. Group, Univ. of Bremen, Bremen ; Kirchner, F.

This paper describes the use of evolutionary algorithms to find an optimal solution for the parameters of an impedance controller represented as an artificial neural network (ANN). An impedance controller with force tracking capabilities has been evolved using evolutionary strategies which control the forces between a robotic manipulator and the environment. Simulation results show the controllerpsilas performance using a model of a two-link robot arm and a Hunt-Crossley non-linear model of the environment.

Published in:

Emerging Technologies and Factory Automation, 2008. ETFA 2008. IEEE International Conference on

Date of Conference:

15-18 Sept. 2008