Powered Prosthesis Locomotion on Varying Terrains: Model-Dependent Control with Real-Time Force Sensing
- Creators
-
Gehlhar, Rachel
-
Yang, Je-han
-
Ames, Aaron D.
Abstract
Lower-limb prosthesis wearers are more prone to falling than non-amputees. Powered prostheses can reduce this instability of passive prostheses. While shown to be more stable in practice, powered prostheses generally use model-independent control methods that lack formal guarantees of stability and rely on heuristic tuning. Recent work overcame one of the limitations of model-based prosthesis control by developing a class of provably stable prosthesis controllers that only require the human interaction forces with the prosthesis, yet these controllers have not been realized with sensing of these forces in the control loop. Our work realizes the first model-dependent prosthesis knee controller that uses in-the-loop on-board real-time force sensing at the interface between the human and prosthesis and at the ground. The result is an optimization-based control methodology that formally guarantees stability while enabling human-prosthesis walking on a variety of terrain types. Experimental results demonstrate this force-based controller outperforms similar controllers not using force sensors, improving tracking across 4 terrain types.
Additional Information
© 2022 IEEE. Manuscript received September 9, 2021; accepted February 7, 2022. Date of publication February 28, 2022; date of current version March 10, 2022. This letter was recommended for publication by Associate Editor J.-J. Cabibihan and Editor P. Valdastri upon evaluation of the reviewers' comments. This work was supported in part by the NSF GRF under Grant DGE-1745301, in part by the NSF under Awards 1923239 and 1924526, and in part by Wandercraft under Award WANDERCRAFT.21. This research was approved by the California Institute of Technology Institutional Review Board with protocol no. 21-0693 for human subject testing.Attached Files
Accepted Version - Powered_Prosthesis_Locomotion_on_Varying_Terrains_Model-Dependent_Control_with_Real-Time_Force_Sensing.pdf
Files
Name | Size | Download all |
---|---|---|
md5:801747d6922855e65869b1696a98d4c0
|
3.7 MB | Preview Download |
Additional details
- Eprint ID
- 113763
- DOI
- 10.1109/lra.2022.3154810
- Resolver ID
- CaltechAUTHORS:20220307-188387000
- NSF Graduate Research Fellowship
- DGE-1745301
- NSF
- CMMI-1923239
- NSF
- ECCS-1924526
- Wandercraft
- WANDERCRAFT.21
- Created
-
2022-03-08Created from EPrint's datestamp field
- Updated
-
2022-03-22Created from EPrint's last_modified field