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Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control.

Naser Mehrabi1, Reza Sharif Razavian1, Borna Ghannadi1

  • 1Systems Design Engineering, University of Waterloo Waterloo, ON, Canada.

Frontiers in Computational Neuroscience
|January 31, 2017
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Summary
This summary is machine-generated.

Optimal feedback control using nonlinear model predictive control (NMPC) effectively predicts human arm trajectories for reaching tasks. However, NMPC shows limitations in accurately predicting hand velocity and muscle activation patterns.

Keywords:
NMPCmotor controlprediction horizonreaching

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Area of Science:

  • Robotics and Control Systems
  • Biomechanics
  • Neuroscience

Background:

  • Human arm movements involve complex trajectory planning.
  • Optimal control strategies are hypothesized to underlie voluntary movements.
  • Previous models often require extensive experimental data.

Purpose of the Study:

  • To apply nonlinear model predictive control (NMPC) for trajectory planning in human arm movements.
  • To investigate the predictive capabilities of NMPC without motion tracking or EMG data.
  • To analyze the influence of prediction horizon on movement outcomes.

Main Methods:

  • A two-degree-of-freedom musculoskeletal planar arm model was simulated.
  • An NMPC with a finite prediction horizon was implemented as the feedback controller.
  • The model assumed minimization of muscular effort during goal-directed movements.

Main Results:

  • NMPC accurately predicted hand trajectories for reaching fixed and moving targets.
  • NMPC predictions aligned well with dynamic optimization and experimental trajectories.
  • NMPC showed less agreement with experimental and dynamic optimization results for hand velocity and muscle activations.

Conclusions:

  • NMPC is a viable, data-independent approach for predicting human arm reaching trajectories.
  • The study highlights discrepancies between predicted and actual hand velocity and muscle activity.
  • Further refinement of NMPC models may be needed to capture nuanced aspects of motor control.