By Topic

Adaptive impedance control based on dynamic recurrent fuzzy neural network for upper-limb rehabilitation robot

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)
Guozheng Xu ; Jiangsu Province Key Lab. of Remote Meas. & Control, Southeast Univ., Nanjing, China ; Aiguo Song

The controller design is one of the major difficulties in realizing robot-aided rehabilitation program. The purpose of our study is to develop an adaptive impedance force control strategy based on dynamic recurrent fuzzy neural network to maintain the stability of the rehabilitation robot system in the case when the patient's physical condition makes a change. An on-line identification method was used to estimate impaired limb's mechanical impedance parameters. By using dynamic recurrent fuzzy neural network, desired impedance control parameters between rehabilitation robotic end-effector and upper-limb were regulated through on-line learning according to the estimated impaired limb's mechanical impedance parameters. Analysis and simulation results indicate that the proposed algorithm is much more stable and smooth than other impedance control methods.

Published in:

Control and Automation, 2009. ICCA 2009. IEEE International Conference on

Date of Conference:

9-11 Dec. 2009