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Related Experiment Videos

Joint angle control by FES using a feedback error learning controller.

Kenji Kurosawa1, Ryoko Futami, Takashi Watanabe

  • 1Department of Electrical Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan. kurosawa@nrips.go.jp

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|October 5, 2005
PubMed
Summary

This study explored a feedback error learning (FEL) controller for functional electrical stimulation (FES) to improve wrist movement control. The FEL controller demonstrated superior performance over traditional PID methods, reducing errors and response delays.

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Area of Science:

  • Biomedical Engineering
  • Control Systems
  • Neurorehabilitation

Background:

  • Functional Electrical Stimulation (FES) is used to restore motor function.
  • Developing effective FES controllers is crucial for improving movement control.
  • Hybrid control strategies offer potential for enhanced FES performance.

Purpose of the Study:

  • To investigate the efficacy of a feedback error learning (FEL) scheme for an FES controller.
  • To compare the performance of the FEL controller against a conventional PID controller.
  • To optimize FEL controller parameters using simulation and an artificial forward model.

Main Methods:

  • Implemented a hybrid FEL controller combining a four-layered neural network and a PID controller.
  • Utilized an artificial neural network to create a forward model of wrist dynamics for parameter tuning.

Related Experiment Videos

  • Conducted computer simulations and experiments with six able-bodied subjects controlling wrist palmar/dorsi-flexion via FES.

    • Main Results:

    • The FEL controller successfully controlled wrist movement and outperformed the conventional PID controller.
    • FEL resulted in decreased average tracking error and reduced response delay for fast movements (2s cycle period).
    • Pre-training the feedforward component with an artificial forward model shortened learning iterations.

    Conclusions:

    • The FEL scheme is a viable and effective approach for FES control of wrist movement.
    • FEL offers significant advantages over conventional PID control in terms of accuracy and responsiveness.
    • Utilizing artificial models for pre-training can accelerate the learning process of FEL controllers.