By Topic

Adaptive repetitive learning control of robotic manipulators without the requirement for initial repositioning

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

3 Author(s)
Mingxuan Sun ; Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore ; Shuzhi Sam Ge ; Mareels, I.M.Y.

This paper presents adaptive repetitive learning control for trajectory tracking of uncertain robotic manipulators. Through the introduction of a novel Lyapunov-like function, the proposed method only requires the system to start from where it stopped at the last cycle, and avoids the strict requirement for initial repositioning for all the cycles. In addition, it is more applicable, as it only requires the variables to be learned in an iteration-independent manner, rather than satisfying the periodicity requirement in a number of the conventional methods. With the adoption of fully saturated learning, all the signals in the closed loop are guaranteed to be bounded, and the iterative trajectories are proven to follow the profiles of desired trajectories over the entire operation interval. The effectiveness of the proposed method is shown through extensive numerical simulation results.

Published in:

Robotics, IEEE Transactions on  (Volume:22 ,  Issue: 3 )