A Study on Feedback Error Learning Controller for Functional Electrical Stimulation: Generation of Target Trajectories by Minimum Jerk Model

Takashi Watanabe, Keisuke Fukushima

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The Feedback Error Learning controller was found to be applicable to functional electrical stimulation control of wrist joint movements in control with subjects and computer simulation tests in our previous studies. However, sinusoidal trajectories were only used for the target joint angles and the artificial neural network (ANN) was trained for each trajectory. In this study, focusing on two-point reaching movement, target trajectories were generated by the minimum jerk model. In computer simulation tests, ANNs trained with different number of target trajectories under the same total number of control iterations (50 control trials) were compared. The inverse dynamics model (IDM) of the controlled limb realized by the trained ANN decreased the output power of the feedback controller and improved tracking performance to unlearned target trajectories. The IDM performed most effectively when target trajectory was changed every one control trial during ANN training.

Original languageEnglish
Pages (from-to)270-274
Number of pages5
JournalArtificial Organs
Volume35
Issue number3
DOIs
Publication statusPublished - 2011 Mar 1

Keywords

  • Feedback error learning
  • Functional electrical stimulation
  • Minimum jerk model
  • Tow-point reaching movement

ASJC Scopus subject areas

  • Bioengineering
  • Medicine (miscellaneous)
  • Biomaterials
  • Biomedical Engineering

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