By Topic

Neural network output feedback control of robot manipulators

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)
Kim, Y.H. ; Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, USA ; Lewis, F.L.

A robust neural network output feedback scheme is developed for the motion control of robot manipulators without measuring joint velocities. A neural network observer is presented to estimate the joint velocities. It is shown that all the signals in a closed-loop system composed of a robot, an observer, and a controller is uniformly ultimately bounded. This amounts to a separation principle for the design of nonlinear dynamic trackers for robotic systems. The neural network weights in both the observer and the controller are tuned online, with no off-line learning phase required. No exact knowledge of the robot dynamics is required so that the neural network controller is model-free and so applicable to a class of nonlinear systems which have a similar structure to robot manipulators. Simulation results on 2-link robot manipulator are reported to show the performance of the proposed output feedback control scheme

Published in:

Robotics and Automation, IEEE Transactions on  (Volume:15 ,  Issue: 2 )