Authors:
Zunaed Kibria
and
Sesh Commuri
Affiliation:
Electrical and Biomedical Department, University of Nevada - Reno, Reno, Nevada, U.S.A.
Keyword(s):
Prosthetic Control, Radial Basis Function Based Neural Network (RBFNN), Gait Analysis.
Abstract:
Achieving proper post-amputation mobility in an individual is extremely important to ensure the health of the residual limb and the quality of life of an individual. Traditionally, prosthetic limbs were designed to primarily support the weight of the individual and replicate the look and feel of the natural limb. Powered prosthetic devices are typically based on classical control and cannot adapt to changing user requirements. A critical challenge in controller design is that, unlike tracking controllers, the desired trajectory for the prosthetic joint is unknown. Improper control can lead to asymmetry in the gait of intact and amputated sides, which in turn can have adverse health consequences. In this paper, an intelligent controller for above-knee prosthesis is proposed that can generate pseudo-trajectories for the joints, learn the dynamics of the prosthetic limb in real-time, and track these pseudo-trajectories to reduce the asymmetry in gait between the intact and amputated sid
e. Mathematical analysis shows that the method is stable and can adapt to changing user gaits. Numerical simulations and Monte Carlo analysis show that the performance of the controller is robust to variations in dynamics and user requirements, and results in near-natural gait for the individual.
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