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Frequency sensitive motion control for a single joint arm model.

Hyunjoo Park1, Dominique M Durand

  • 1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
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This study introduces a novel feedforward controller combining lookup table and artificial neural network (ANN) methods. The hybrid controller significantly reduces output errors in musculoskeletal models, improving tracking for rapid movements.

Area of Science:

  • Biomedical Engineering
  • Robotics
  • Control Systems

Background:

  • Accurate control of musculoskeletal models is crucial for prosthetics and rehabilitation robotics.
  • Traditional controllers often struggle with the complex, time-varying dynamics of biological systems, especially during rapid movements.
  • Existing methods may exhibit delayed tracking responses, limiting performance in dynamic applications.

Purpose of the Study:

  • To develop and evaluate a novel hybrid feedforward control strategy for musculoskeletal systems.
  • To improve the tracking accuracy and reduce output errors compared to conventional controllers.
  • To address the limitations of lookup table controllers in compensating for rapid movements.

Main Methods:

  • A feedforward controller integrating a lookup table controller and an artificial neural network (ANN) controller was designed.

Related Experiment Videos

  • The lookup table controller stores steady-state muscle activation-output relationships.
  • The ANN controller was trained to learn the inverse dynamics of the musculoskeletal model to compensate for tracking delays.
  • A decision block dynamically adjusted the contribution of each controller based on reference trajectory frequency analysis.
  • The control algorithm was implemented and tested on a single-joint elbow model.

    • Main Results:

    • The combined feedforward controller demonstrated reduced overall output errors.
    • The artificial neural network component effectively compensated for the lookup table controller's limitations during rapid movements.
    • The decision block successfully modulated controller contributions based on movement dynamics.

    Conclusions:

    • The proposed hybrid feedforward controller offers a significant improvement in controlling musculoskeletal models.
    • This approach enhances tracking performance, particularly for dynamic and rapid movements.
    • The integration of lookup tables and ANNs provides a robust and adaptable control solution for biomechatronic systems.