Iterative learning control for a musculoskeletal arm: Utilizing multiple space variables to improve the robustness

Kenji Tahara, Yuta Kuboyama, Ryo Kurazume

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, a new iterative learning control method which uses multiple space variables for a musculoskeletal-like arm system is proposed to improve the robustness against noises being included in sensory information. In our previous works, the iterative learning control method for the redundant musculoskeletal arm to acquire a desired endpoint trajectory simultaneous with an adequate internal force was proposed. The controller was designed using only muscle space variables, such as a muscle length and contractile velocity. It is known that the movement of the musculoskeletal system can be expressed in a hierarchical three-layered space which is composed of the muscle space, the joint space and the task space. Thus, the new iterative learning control input is composed of multiple space variables to improve its performance and robustness. Numerical simulations are conducted and their result is evaluated from the viewpoint of the robustness to noises of sensory information. An experiment is performed using a prototype of musculoskeletal-like manipulator, and the practical usefulness of the proposed method is demonstrated through the result.

Original languageEnglish
Title of host publication2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
Pages4620-4625
Number of pages6
DOIs
Publication statusPublished - Dec 1 2012
Event25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve, Portugal
Duration: Oct 7 2012Oct 12 2012

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
CountryPortugal
CityVilamoura, Algarve
Period10/7/1210/12/12

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Tahara, K., Kuboyama, Y., & Kurazume, R. (2012). Iterative learning control for a musculoskeletal arm: Utilizing multiple space variables to improve the robustness. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012 (pp. 4620-4625). [6385628] (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2012.6385628