2026-09-20T06:49:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1566042024-09-04T03:39:41Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Herr, Hugh
Hamed, Yasin
Massachusetts Institute of Technology. Department of Mechanical Engineering
2024-09-03T21:10:57Z
2024-09-03T21:10:57Z
2024-05
2024-07-10T17:33:28.072Z
https://hdl.handle.net/1721.1/156604
Developments in prosthetic ankles have been improving amputee gaits, gradually approaching a more normal, non-amputee gait as designs develop. Currently, powered ankle
prosthetics, or bionic ankles, hold the potential to mold the amputee gait much closer to its biological counterpart. This requires accurate control of the prosthesis and thus requires an
accurate understanding of the plant model characterizing the bionic ankle. It is typically rather difficult to analytically develop such a model, so this project explores an experimental approach through the use of nonlinear stochastic system identification techniques to characterize the prosthetic. Results from initial experiments which use the method for characterizing known linear models revealed unaccounted for sources of error and nonlinearity which future work will attempt to remedy to continue developing such a characterization fixture.
S.B.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Design of a Testing Fixture for the Characterization and Modeling of a Powered Prosthetic Ankle Using a Lorentz Force Actuator
Thesis
application/pdf
Bachelor
Bachelor of Science in Mechanical Engineering
Publication
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unknown
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<Abstract>Developments in prosthetic ankles have been improving amputee gaits, gradually approaching a more normal, non-amputee gait as designs develop. Currently, powered ankle
prosthetics, or bionic ankles, hold the potential to mold the amputee gait much closer to its biological counterpart. This requires accurate control of the prosthesis and thus requires an
accurate understanding of the plant model characterizing the bionic ankle. It is typically rather difficult to analytically develop such a model, so this project explores an experimental approach through the use of nonlinear stochastic system identification techniques to characterize the prosthetic. Results from initial experiments which use the method for characterizing known linear models revealed unaccounted for sources of error and nonlinearity which future work will attempt to remedy to continue developing such a characterization fixture.</Abstract>
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