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Nonlinear Model Predictive Control of Functional Electrical Stimulation

[+] Author Affiliations
Nicholas A. Kirsch, Naji A. Alibeji, Nitin Sharma

University of Pittsburgh, Pittsburgh, PA

Paper No. DSCC2015-9762, pp. V002T27A005; 8 pages
doi:10.1115/DSCC2015-9762
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME

abstract

One of the major limitations of functional electrical stimulation (FES) is the rapid onset of muscle fatigue. Minimizing stimulation is the key to decreasing the adverse effects of muscle fatigue caused by FES. Optimal control can be used to compute the minimum amount of stimulation necessary to produce a desired motion. In this paper, a gradient projection-based model predictive controller is used for an approximate optimal control of a knee extension neuroprosthesis. A control Lyapunov function is used as a terminal cost to ensure stability of the model predictive control.

Copyright © 2015 by ASME

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