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  4. Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system

Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system

Med Biol Eng Comput, 2009 · DOI: 10.1007/s11517-009-0479-3 · Published: May 1, 2009

BioinformaticsBiomedical

Simple Explanation

This paper presents a control system for arm movement using functional electrical stimulation (FES) in individuals with high-level spinal cord injuries. The system combines feedforward and feedback control strategies. The feedforward controller anticipates the muscle activation needed for movement, while the feedback controller adjusts for errors caused by fatigue or external forces. The controller was designed and tested using a computer model of the arm, demonstrating good accuracy even with simulated muscle fatigue and disturbances.

Study Duration
Not specified
Participants
Not specified
Evidence Level
Not specified

Key Findings

  • 1
    The combined feedforward-feedback controller achieved a tracking error of less than 4° in ideal conditions.
  • 2
    Even with considerable fatigue and external disturbances, the tracking error remained less than 10°.
  • 3
    The feedforward component improved overall controller performance and reduced the magnitude and variability of muscle activations compared to feedback-only control.

Research Summary

This paper introduces a feedforward–feedback controller for a two-joint, six-muscle arm model, designed for functional electrical stimulation (FES) systems. The controller incorporates an artificial neural network (ANN) to approximate the inverse dynamics of the arm in the feedforward component and a neuro-PID controller in the feedback loop to address the nonlinearities of the musculoskeletal system. Simulation results demonstrate excellent tracking performance during goal-oriented movements, with robustness against muscle fatigue and external disturbances.

Practical Implications

FES Controller Development

The presented controller design can be used as a basis for developing more sophisticated FES systems for upper-extremity rehabilitation in individuals with tetraplegia.

Model-Based Design

The model-based approach facilitates the exploration of various control strategies before human implementation, reducing the need for invasive and expensive procedures.

Clinical Application

The control strategy can be further adapted and customized to individual patients, incorporating patient-specific arm models and adapting to the dynamics of the FES-driven arm to produce more accurate movements.

Study Limitations

  • 1
    The model does not capture the complexities of a full arm model
  • 2
    The fatigue model used was fairly simplistic.
  • 3
    Numerical approximations prevent perfect inversion of the model.

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