By Topic

Representation and learning of nonlinear compliance using neural nets

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
$33 $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

1 Author(s)
H. Asada ; Dept. of Mech. Eng., Massachusetts Inst. of Technol., Cambridge, MA, USA

A new approach to compliant motion control using neural networks is presented. In the paper, “compliance” is treated as a nonlinear mapping from a measured force to a corrected motion. The nonlinear mapping by a multilayer neural network is outlined, this allows one to deal with complex control strategies that cannot be represented by linear compliance, such as in stiffness and damping control

Published in:

IEEE Transactions on Robotics and Automation  (Volume:9 ,  Issue: 6 )