Functional electrical stimulation (FES) is commonly
used for people with neurological conditions. As
the muscle geometry changes (i.e., muscle lengthening/
shortening), the force induced by static electrode placement
may also change. Experimental results indicate that
muscle forces can be increased by spatially switching
stimulation as the muscle geometry changes with joint
angle. In this letter, an electric field is switched between
multiple electrodes placed across the biceps brachii to
track a desired trajectory. A switched systems approach
is used to develop a position-based switching law, including
a switched robust sliding mode controller that successfully
tracks the desired angular trajectory about the
elbow, despite changes in muscle geometry. Lyapunovbased
methods for switched systems are used to prove
global exponential tracking. Experimental results from nine
able-bodied subjects are presented and the developed control
system achieves an average position and velocity error
of −0.21 ± 1.17 deg and −0.43 ± 5.38 deg/s, respectively,
and, on average, reduces fatigue by 13.6%, as compared
to traditional single-electrode methods, demonstrating the
performance of the uncertain nonlinear switched control
system.
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Adaptive Trajectory Tracking During Motorized and FES-Induced Biceps Curls via Integral Concurrent Learning
A common rehabilitative technique for those with neuro-muscular disorders is functional electrical stimulation (FES) induced exercise such as FES-induced biceps curls. FES has been shown to have numerous health benefits, such as increased muscle mass and retraining of the nervous system. Closed-loop control of a motorized FES system presents numerous challenges since the system has nonlinear and uncertain dynamics and switching is required between motor and FES control, which is further complicated by the muscle having an uncertain control effectiveness. An additional complication of FES systems is that high gain feedback from traditional robust controllers can be uncomfortable to the participant. In this paper, data-based, opportunistic learning is achieved by implementing an integral concurrent learning (ICL) controller during a motorized and FES-induced biceps curl exercise. The ICL controller uses adaptive feedforward terms to augment the FES controller to reduce the required control input. A Lyapunov-based analysis is performed to ensure exponential trajectory tracking and opportunistic, exponential learning of the uncertain human and machine parameters. In addition to improved tracking performance and robustness, the potential of learning the specific dynamics of a person during a rehabilitative exercise could be clinically significant. Preliminary simulation results are provided and demonstrate an average position error of 0.14 ± 1.17 deg and an average velocity error of 0.004 ± 1.18 deg/s.
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- Award ID(s):
- 1762829
- NSF-PAR ID:
- 10231097
- Date Published:
- Journal Name:
- ASME Dynamic Systems and Control Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DSCC2020-3125
