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Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks.

Alessandra Pedrocchi1, Simona Ferrante, Elena De Momi

  • 1Nitlab, Bioengineering Department, Politecnico di Milano, Milano, Italy. alessandra.pedrocchi@polimi.it

Journal of Neuroengineering and Rehabilitation
|October 13, 2006
PubMed
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The novel error mapping controller (EMC) enhances neuroprostheses by improving tracking accuracy and prolonging exercise duration. This artificial neural network-based controller manages fatigue, offering a more robust and user-friendly solution for clinical practice.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Control Systems

Background:

  • Neuroprostheses controller design faces challenges due to physiological variability and non-stationary behavior.
  • Clinical use requires controllers that are easy to operate without extensive setup.

Purpose of the Study:

  • To develop and evaluate an Error Mapping Controller (EMC) for neuroprostheses.
  • To improve controller performance in terms of accuracy, fatigue management, and robustness.

Main Methods:

  • Utilized artificial neural networks (ANNs) for inverse model and feedback controller design within the EMC.
  • Validated controller performance using a neuromuscular model and compared EMC against PIDAW and NEUROPID controllers.
  • Assessed EMC robustness against plant parameter variations and mechanical disturbances.

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Main Results:

  • The EMC demonstrated superior tracking accuracy compared to PIDAW and NEUROPID.
  • EMC effectively managed fatigue, prolonging exercise duration and preventing muscle overstress.
  • EMC exhibited enhanced robustness to parameter variations and mechanical disturbances.

Conclusions:

  • The EMC offers a balanced approach to tracking accuracy and fatigue management, enabling extended movement duration.
  • The controller's training set collection is adaptable for routine clinical practice, simplifying operator requirements.