Journal article
Pablo Delgado, Nathan Gonzalez, Yimesker Yihun
Machines, 2023
Machines, 2023
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DOI
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APA
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Delgado, P., Gonzalez, N., & Yihun, Y. (2023). Integration of sEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System. Machines.
Chicago/Turabian
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Delgado, Pablo, Nathan Gonzalez, and Yimesker Yihun. “Integration of SEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System.” Machines (2023).
MLA
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Delgado, Pablo, et al. “Integration of SEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System.” Machines, 2023.
BibTeX Click to copy
@article{pablo2023a,
title = {Integration of sEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System},
year = {2023},
journal = {Machines},
author = {Delgado, Pablo and Gonzalez, Nathan and Yihun, Yimesker}
}
Abstract
This paper presents an adaptive Fuzzy Sliding Mode Control approach for an Assist-as-Needed (AAN) strategy to achieve effective human–exoskeleton synergy. The proposed strategy employs an adaptive instance-based learning algorithm to estimate muscle effort, based on surface Electromyography (sEMG) signals. To determine and control the inverse dynamics of a highly nonlinear 4-degrees-of-freedom exoskeleton designed for upper-limb therapeutic exercises, a modified Recursive Newton-Euler Algorithm (RNEA) with Sliding Mode Control (SMC) was used. The exoskeleton position error and raw sEMG signal from the bicep’s brachii muscle were used as inputs for a fuzzy inference system to produce an output to adjust the sliding mode control law parameters. The proposed robust control law was simulated using MATLAB-Simulink, and the results showed that it could instantly adjust the necessary support, based on the combined motion of the human–exoskeleton system’s muscle engagement, while keeping the state trajectory errors and input torque bounded within ±5×10−2 rads and ±5 N.m, respectively.