Gehlhar, Rachel and Chen, Yuxiao and Ames, Aaron D. (2020) Data-driven Characterization of Human Interaction for Model-based Control of Powered Prostheses. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE , Piscataway, NJ, pp. 4126-4133. ISBN 978-1-7281-6212-6. https://resolver.caltech.edu/CaltechAUTHORS:20200527-124525913
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Abstract
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: | Book Section | ||||||||||||
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Additional Information: | © 2020 IEEE. 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. | ||||||||||||
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DOI: | 10.1109/IROS45743.2020.9341388 | ||||||||||||
Record Number: | CaltechAUTHORS:20200527-124525913 | ||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20200527-124525913 | ||||||||||||
Official Citation: | R. Gehlhar, Y. Chen and A. D. Ames, "Data-driven Characterization of Human Interaction for Model-based Control of Powered Prostheses," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020, pp. 4126-4133, doi: 10.1109/IROS45743.2020.9341388 | ||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
ID Code: | 103491 | ||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||
Deposited By: | Tony Diaz | ||||||||||||
Deposited On: | 27 May 2020 21:22 | ||||||||||||
Last Modified: | 16 Nov 2021 18:22 |
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