An extremum seeking method for non-isometric neuromuscular electrical stimulation

An optimal extremum seeking approach is developed in this paper to identify frequency and voltage modulation parameters for a neuromuscular electrical stimulation control objective. The control objective is to externally apply optimally varied voltage or frequency modulation parameters to a human quadriceps muscle to generate a desired knee joint angle. Experimental results are provided to illustrate the limb positioning performance of a real-time extremum seeking routine (i.e., Brent's Method).

[1]  Miroslav Krstic,et al.  Stabilization of Nonlinear Uncertain Systems , 1998 .

[2]  J. Abbas,et al.  Feedback control of coronal plane hip angle in paraplegic subjects using functional neuromuscular stimulation , 1991, IEEE Transactions on Biomedical Engineering.

[3]  P. H. Peckham.  FUNCTIONAL NEUROMUSCULAR STIMULATION. , 1981 .

[4]  O. Jacobs,et al.  Design of a single-input sinusoidal-perturbation extremum-control system , 1968 .

[5]  James J. Abbas,et al.  Sensitivity and versatility of an adaptive system for controlling cyclic movements using functional neuromuscular stimulation , 2000, IEEE Transactions on Biomedical Engineering.

[6]  D. Graupe,et al.  Stochastically-modulated stimulation to slow down muscle fatigue at stimulated sites in paraplegics using functional electrical stimulation for leg extension , 2000, Neurological research.

[7]  Peckham Ph,et al.  Functional neuromuscular stimulation (FNS). , 1996 .

[8]  Stuart A Binder-Macleod,et al.  Effects of stimulation frequencies and patterns on performance of repetitive, nonisometric tasks. , 2002, Journal of applied physiology.

[9]  P. Veltink,et al.  Cycle-to-cycle control of swing phase of paraplegic gait induced by surface electrical stimulation , 1995, Medical and Biological Engineering and Computing.

[10]  D. Graupe,et al.  Control of neuromuscular stimulation for ambulation by complete paraplegics via artificial neural networks , 2001, Neurological research.

[11]  Warren E. Dixon,et al.  Extremum-seeking nonlinear controllers for a human exercise machine , 2006 .

[12]  P. Crago,et al.  Feedback control of electrically stimulated muscle using simultaneous pulse width and stimulus period modulation , 1991, IEEE Transactions on Biomedical Engineering.

[13]  S. Jonic,et al.  Three machine learning techniques for automatic determination of rules to control locomotion , 1999, IEEE Transactions on Biomedical Engineering.

[14]  M. Popovic,et al.  The effect of random modulation of functional electrical stimulation parameters on muscle fatigue , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Miroslav Krstic,et al.  Real-Time Optimization by Extremum-Seeking Control: Ariyur/Extremum Seeking , 2004 .

[16]  P. Hunter Peckham,et al.  Principles of electrical stimulation , 1999 .

[17]  Ning Lan,et al.  Neural network generation of muscle stimulation patterns for control of arm movements , 1994 .

[18]  F. Zajac,et al.  Paraplegic standing controlled by functional neuromuscular stimulation. II. Computer simulation studies , 1989, IEEE Transactions on Biomedical Engineering.

[19]  J Riess,et al.  Adaptive neural network control of cyclic movements using functional neuromuscular stimulation. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[20]  M. Krstić,et al.  Real-Time Optimization by Extremum-Seeking Control , 2003 .

[21]  Darren M. Dawson,et al.  QMotor 3.0 and the QMotor robotic toolkit: a PC-based control platform , 2002 .

[22]  H.J. Chizeck,et al.  Control of end-point forces of a multijoint limb by functional neuromuscular stimulation , 1991, IEEE Transactions on Biomedical Engineering.

[23]  P. Crago,et al.  Feedback control methods for task regulation by electrical stimulation of muscles , 1991, IEEE Transactions on Biomedical Engineering.

[24]  M. Popovic,et al.  Reducing muscle fatigue due to functional electrical stimulation using random modulation of stimulation parameters. , 2005, Artificial organs.

[25]  K. Y. Tong,et al.  Gait control system for functional electrical stimulation using neural networks , 2006, Medical & Biological Engineering & Computing.