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Data-driven Characterization of Human Interaction for Model-based Control of Powered Prostheses

Gehlhar, Rachel and Chen, Yuxiao and Ames, Aaron D. (2020) Data-driven Characterization of Human Interaction for Model-based Control of Powered Prostheses. . (Unpublished)

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This paper proposes a data-driven method for powered prosthesis control that achieves stable walking without the need for additional sensors on the human. The key idea is to extract the nominal gait and the human interaction information from motion capture data, and reconstruct the walking behavior with a dynamic model of the human-prosthesis system. The walking behavior of a human wearing a powered prosthesis is obtained through motion capture, which yields the limb and joint trajectories. Then a nominal trajectory is obtained by solving a gait optimization problem designed to reconstruct the walking behavior observed by motion capture. Moreover, the interaction force profiles between the human and the prosthesis are recovered by simulating the model following the recorded gaits, which are then used to construct a force tube that covers all the interaction force profiles. Finally, a robust Control Lyapunov Function (CLF) Quadratic Programming (QP) controller is designed to guarantee the convergence to the nominal trajectory under all possible interaction forces within the tube. Simulation results show this controller's improved tracking performance with a perturbed force profile compared to other control methods with less model information.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Gehlhar, Rachel0000-0002-4838-8839
Chen, Yuxiao0000-0001-5276-7156
Ames, Aaron D.0000-0003-0848-3177
Additional Information:This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1745301 and NSF NRI Grant No. 1724464.
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Record Number:CaltechAUTHORS:20200527-124525913
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103491
Deposited By: Tony Diaz
Deposited On:27 May 2020 21:22
Last Modified:07 Jul 2020 19:31

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