An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton

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dc.contributor.authorMolazadeh, Vahidreza
dc.contributor.authorZhang, Qiang
dc.contributor.authorBao, Xuefeng
dc.contributor.authorSharma, Nitin
dc.date.accessioned2025-01-09T19:43:00Z
dc.date.available2025-01-09T19:43:00Z
dc.date.copyright© 2022 IEEE
dc.date.issued2022
dc.description.abstractA hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions through FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this article, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.
dc.format.mimetypeapplication/pdf
dc.identifier.citationV. Molazadeh, Q. Zhang, X. Bao and N. Sharma, "An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton," in IEEE Transactions on Control Systems Technology, vol. 30, no. 3, pp. 1021-1036, May 2022, doi: 10.1109/TCST.2021.3089885.
dc.identifier.doidoi: 10.1109/TCST.2021.3089885.
dc.identifier.issn1558-0865
dc.identifier.orcidhttps://orcid.org/0000-0002-8806-9672
dc.identifier.urihttps://ir.ua.edu/handle/123456789/15128
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEE
dc.rights.licenseThis work is licensed under a CC BY-NC 4.0 license.
dc.subjectFunctional electrical stimulation (FES)
dc.subjecthybrid exoskeleton
dc.subjectiterative learning control
dc.subjectneural networks (NNs)
dc.subjectpowered exoskeleton
dc.subjectvirtual constraints
dc.titleAn Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton
dc.typeArticle
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