By Topic

Iterative learning control for a redundant musculoskeletal arm: Acquisition of adequate internal force

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)
Tahara, K. ; Inst. for Adv. Study, Kyushu Univ., Fukuoka, Japan ; Kino, H.

This paper presents a proposal of an iterative learning control method for a musculoskeletal arm to acquire adequate internal force to realize human-like natural movements. Additionally, a dynamic damping ellipsoid at the end-point is introduced to evaluate internal forces obtained through the iterative learning. In our previous works, we have presented that a human-like smooth reaching movement using a musculoskeletal redundant arm model can be obtained by introducing a nonlinear muscle model and “the Virtual spring-damper hypothesis”. However, the internal forces have been determined heuristically, so far. In this paper, an iterative learning control method is used for acquisition of an adequate dynamic damping ellipsoid according to a given task, in order to determine internal forces more systematically. It is presented that the learning control scheme can perform effectively to realize given desired tasks, even under the existence of strong nonlinear characteristics of the muscles. After acquiring a given task, the dynamic damping ellipsoid is introduced to evaluate the relation between a damping effect generated by the acquired internal forces and a trajectory of the end-point. Some numerical simulations are performed and the usefulness of the learning control strategy, despite strong nonlinearity of the muscles, is demonstrated through these results.

Published in:

Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on

Date of Conference:

18-22 Oct. 2010