Name
Abstract | ||
|---|---|---|
Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length and velocity relationships and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network (NN)-based methods. Efforts in this paper focus on the use of a NN feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result (in lieu of typical uniformly ultimately bounded stability). Specifically, an NN-based controller and Lyapunov-based stability analysis are provided to enable semi-global asymptotic tracking of a desired limb time-varying trajectory (i.e., non-isometric contractions). The developed controller is applied as an amplitude modulated voltage to external electrodes attached to the distal-medial and proximal-lateral portion of the quadriceps femoris muscle group in non-impaired volunteers. The added value of incorporating a NN feedforward term is illustrated through experiments that compare the developed controller with and without the NN feedforward component. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TCST.2011.2125792 | Control Systems Technology, IEEE Transactions |
Keywords | Field | DocType |
Lyapunov methods,closed loop systems,feedback,feedforward neural nets,medical control systems,muscle,neural nets,time-varying systems,Lyapunov-based stability analysis,NN feedforward controller,NN-based controller,closed-loop neural network-based NMES control,continuous robust feedback,human limb tracking,limb time-varying trajectory,neuromuscular electrical stimulation,nonlinear muscle force,pure feedback methods,skeletal muscle,time-varying uncertainties,Asymptotic stability,closed-loop control of functional electrical stimulation (FES),neural networks (NNs),neuromuscular electrical stimulation (NMES),non-isometric contractions,nonlinear control,robust integral of the sign of the error (RISE)-based feedback | Control theory,Feedforward neural network,Nonlinear system,Control theory,Nonlinear control,Control engineering,Exponential stability,Artificial neural network,Mathematics,Trajectory,Feed forward | Journal |
Volume | Issue | ISSN |
20 | 3 | 1063-6536 |
Citations | PageRank | References |
23 | 1.71 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nitin Sharma | 1 | 70 | 9.45 |
| Chris M. Gregory | 2 | 25 | 2.30 |
| Marcus Johnson | 3 | 139 | 8.47 |
| Warren E. Dixon | 4 | 1757 | 149.42 |